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Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM. [ABSTRACT FROM AUTHOR]

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In contrast to robots, animals are born with the capability to efficiently and robustly navigate in nature, and bionic SLAM algorithms have received increasing attention recently. Current bionic SLAM algorithms, including RatSLAM, with relatively low accuracy and robustness, tend to fail in certain challenging environments. In order to design a bionic SLAM system with a novel framework and relatively high practicality, and to facilitate the development of bionic SLAM research, in this paper we present LFVB-BioSLAM, a bionic SLAM system with a light-weight LiDAR-based front end and a bio-inspired vision-based back end. We adopt a range flow-based LiDAR odometry as the front end of the SLAM system, providing the odometry estimation for the back end, and we propose a biologically-inspired back end processing algorithm based on the monocular RGB camera, performing loop closure detection and path integration. Our method is verified through real-world experiments, and the results show that LFVB-BioSLAM outperforms RatSLAM, a vision-based bionic SLAM algorithm, and RF2O, a laser-based horizontal planar odometry algorithm, in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Place recognition is an important branch of Simultaneous Localization and Mapping (SLAM), which finds revisited places, thereby reducing error accumulations. Most mainstream methods only concentrate on the similarity of two LiDAR scans and cannot obtain the pose information. In this paper, we design a descriptor named Occupied Place Description (OPD) and propose an efficient map-aided place recognition and localization method. Our method can effectively detect loop events, obtain the vehicle's poses, and overcome the overlap dependence of pairwise point cloud registration. It is composed of four modules. First, the map data generation is designed to store the map database as binary files. Then, a trajectory initialization consisting of global search, local search, and trajectory score computation is proposed to determine the vehicle's initial poses. Next, an online localization method is developed by combining descriptor similarity and Kalman Filtering, which only takes less than 5ms per localization. Finally, loop events are detected based on pose information. Exhaustive evaluations on KITTI and MulRan datasets demonstrate the superiority of our method. The average absolute trajectory error (ATE) is 0.5 m and the average relative rotation error is 2.7 degrees. F 1 score measures the success rate of place recognition and our average maximum F 1 score achieves nearly 0.987. The source code will be available in https://github.com/ShiPC-AI/Occupied-Place-Description. [ABSTRACT FROM AUTHOR]

Loop closure detection can eliminate the accumulated error of visual SLAM, which is of great significance to the SLAM system. Among them, the widely used visual bag-of-word model algorithm has the problem of visual word identity and ambiguity, which affects the loop closure detection effect. To improve these problems and loop back detection effect, this paper proposed a loop closure detection algorithm based on soft-assigned SIFT features. The algorithm allocated the SIFT feature points extracted from the image to several words with the closest Euclidean distance, according to the distance sorting and weighting, and removed the feature points that were far away from the words to generate more discriminating descriptors. In addition, when filtering candidates, the algorithm added the proportion constraint of the same word feature points and the stability constraint of word shift to filter out a few candidates. In the experimental results, the algorithm compared with the traditional visual bag-of-words model and several loop closure detection algorithms in recent years, the recall rate at 100% accuracy rate improved under three data sets, and the average image query time was about 40 ms. The results show that the algorithm can improve the loop detection effect and ensure the real-time performance. [ABSTRACT FROM AUTHOR]

Due to the limitations of LiDAR, such as its high cost, short service life and massive volume, visual sensors with their lightweight and low cost are attracting more and more attention and becoming a research hotspot. As the hardware computation power and deep learning develop by leaps and bounds, new methods and ideas for dealing with visual simultaneous localization and mapping (VSLAM) problems have emerged. This paper systematically reviews the VSLAM methods based on deep learning. We briefly review the development process of VSLAM and introduce its fundamental principles and framework. Then, we focus on the integration of deep learning and VSLAM from three aspects: visual odometry (VO), loop closure detection, and mapping. We summarize and analyze the contribution and weakness of each algorithm in detail. In addition, we also provide a summary of widely used datasets and evaluation metrics. Finally, we discuss the open problems and future directions of combining VSLAM with deep learning. [ABSTRACT FROM AUTHOR]

Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scenes, driverless ground vehicles or robots usually revisit the same place from a different position, meaning that the descriptor cannot give a uniform description of similar scenes, failing LCD. Against this problem, this paper proposes a 3D point cloud descriptor with Twice Projection (3PCD-TP) for the calculation of the similarities between scenes. First, we redefined the origin and primary direction of point clouds according to their distribution and unified their coordinate system, thereby reducing the interference in position recognition due to the rotation and translation of sensors. Next, using the semantic and altitudinal information of point clouds, we generated the 3D descriptor 3PCD-TP with multidimensional features to enhance its ability to describe similar scenes. Following this, we designed a weighting similarity calculation method to reduce the false detection rate of LCD by taking advantage of the property that 3PCD-TP can be projected from multiple angles. Finally, we validated our method using KITTI and the Jilin University (JLU) campus dataset. The experimental results show that our method demonstrated a high level of precision and recall and exhibited greater performance in the face of scenes with reverse loop closure, such as opposite lanes. [ABSTRACT FROM AUTHOR]

Loop closure detection is important for visual simultaneous localization and mapping (VSLAM) systems to reduce cumulative errors and perform re-localization. In order to shorten the loop closure detection online runtime while meeting the accuracy recall rate requirement, the paper proposed a fast loop closure detection algorithm using broad autoencoder (FLCD-BA). The algorithm improved broad learning architecture to learn feature representations autonomously from the input data in an unsupervised manner, and then accomplished loop closure detection tasks. Differing from the traditional deep learning methods, the network used a pseudo-inverse ridge regression algorithm to solve the weight matrix and achieved fast reconfiguration of the network by incremental learning without retraining the entire network. The proposed algorithm experimented on three public datasets without using GPU devices, and the training time of the network greatly reduced compared to bag-of-words models as well as deep learning methods. The experimental results show that the algorithm has high accuracy and recall in detecting loop closure, and the average running time per frame in the test is only 21 ms, which provides a new algorithm for loop closure detection in visual SLAM systems. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Loop closure detection is an important component of Simultaneous Localization and Mapping (SLAM). In this paper, a novel two-branch loop closure detection algorithm unifying deep Convolutional Neural Network (ConvNet) features and semantic edge features is proposed. In detail, we use one feature extraction module to extract both ConvNet and semantic edge features simultaneously. The deep ConvNet features are subjected to a Context Feature Enhancement (CFE) module in the global feature ranking branch to generate a representative global feature descriptor. Concurrently, to reduce the interference of dynamic features, the extracted semantic edge information of landmarks is encoded through the Vector of Locally Aggregated Descriptors (VLAD) framework in the semantic edge feature ranking branch to form semantic edge descriptors. Finally, semantic, visual, and geometric information is integrated by the similarity score fusion calculation. Extensive experiments on six public datasets show that the proposed approach can achieve competitive recall rates at 100% precision compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a feature coding strategy is introduced to extract the shallow geometric features and deep semantic features of images, reduce the amount of image noise information, accelerate the convergence speed of the model, and solve the problems of gradient disappearance and network degradation of deep neural networks. Then, the dynamic routing mechanism of the capsule network is optimized through the entropy peak density, and a vector is used to represent the spatial position relationship between features, which can improve the ability of image feature extraction and expression to optimize the overall performance of networks. Finally, the optimized residual network and capsule network are fused to retain the differences and correlations between features, and the global feature descriptors and feature vectors are combined to calculate the similarity of image features for loop closure detection. The experimental results show that the proposed method can achieve loop closure detection for mobile robots in complex scenes, such as view changes, illumination changes, and dynamic objects, and improve the accuracy and robustness of mobile robot SLAM. [ABSTRACT FROM AUTHOR]

Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task; however, their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this article, we introduce the novel loop closure detection network (LCDNet) that effectively detects loop closures in light detection and ranging (LiDAR) point clouds by simultaneously identifying previously visited places and estimating the six degrees of freedom relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin, especially while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method to improve loop closure detection within the domain of Simultaneous Localization And Mapping (SLAM) by integrating a Multi-Scale Attention and Semantic Guidance (MSA-SG) framework. In SLAM systems, accurate loop closure detection is essential for minimizing localization errors over time and ensuring the reliability of the constructed maps in robotics navigation through uncharted environments. Our proposed method utilizes EfficientNet-EA for robust feature extraction and introduces MSA-SG, a novel mechanism that synergizes multiscale attention with semantic guidance to focus on critical semantic features essential for loop closure detection. This approach ensures the prioritization of static environmental landmarks over transient and irrelevant objects, significantly enhancing the accuracy and efficiency of loop closure detection in complex and dynamic settings. Experimental validations on recognized datasets underscore the superiority of our approach, demonstrating marked improvements in precision, recall, and overall SLAM performance. This research highlights the significant benefits of leveraging semantic insights and attentional focus in advancing the capabilities of loop closure detection for SLAM applications. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86 m and 3.23 m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots. • Introduces a tightly coupled multi-sensor SLAM framework that harnesses the synergies of LiDAR, Camera, IMU, and GNSS. • Laser–visual–inertial odometry fusion system optimizes pose estimation, overcoming limitations of standalone methods. • Visual–laser fusion and GNSS factors improve loop closure efficiency, contributing to precise global pose estimation. • Refines laser–visual–inertial odometry, visual–inertial odometry, IMU preintegration, loop closure, and GNSS factors. • The proposed methodology excels in outdoor scenarios, elevating precision and resilience of SLAM algorithms for mobile robots. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) system typically employs vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination conditions. In scenarios with adverse visibility or in the presence of airborne particulates (e.g., smoke, dust, etc.), alternative modalities such as those based on thermal imaging and inertial sensors are more promising. In this article, we propose the first complete thermal–inertial SLAM system that combines neural abstraction in the SLAM front end with robust pose-graph optimization in the SLAM back end. We model the sensor abstraction in the front end by employing probabilistic deep learning parameterized by mixture density networks (MDNs). Our key strategies to successfully model this encoding from thermal imagery are the usage of normalized 14-b radiometric data, the incorporation of hallucinated visual (RGB) features, and the inclusion of feature selection to estimate the MDN parameters. To enable a full SLAM system, we also design an efficient global image descriptor that is able to detect loop closures from thermal embedding vectors. We performed extensive experiments and analysis using three datasets, namely self-collected ground robot and hand-held data taken in indoor environment, and one public dataset (SubT-tunnel) collected in underground tunnel. Finally, we demonstrate that an accurate thermal–inertial SLAM system can be realized in conditions of both benign and adverse visibility. [ABSTRACT FROM AUTHOR]

Scalable geometry reconstruction and understanding is an important yet unsolved task. Current methods often suffer from false loop closures when there are similar-looking rooms in the scene, and often lack online scene understanding. We propose BuildingFusion, a semantic-aware structural building-scale reconstruction system, which not only allows building-scale dense reconstruction collaboratively, but also provides semantic and structural information on-the-fly. Technically, the robustness to similar places is enabled by a novel semantic-aware room-level loop closure detection(LCD) method. The insight lies in that even though local views may look similar in different rooms, the objects inside and their locations are usually different, implying that the semantic information forms a unique and compact representation for place recognition. To achieve that, a 3D convolutional network is used to learn instance-level embeddings for similarity measurement and candidate selection, followed by a graph matching module for geometry verification. On the system side, we adopt a centralized architecture to enable collaborative scanning. Each agent reconstructs a part of the scene, and the combination is activated when the overlaps are found using room-level LCD, which is performed on the server. Extensive comparisons demonstrate the superiority of the semantic-aware room-level LCD over traditional image-based LCD. Live demo on the real-world building-scale scenes shows the feasibility of our method with robust, collaborative, and real-time performance. [ABSTRACT FROM AUTHOR]

Loop-closure detection (LCD) in large non-stationary environments remains an important challenge in robotic visual simultaneous localization and mapping (vSLAM). To reduce computational and perceptual complexity, it is helpful if a vSLAM system has the ability to perform image change detection (ICD). Unlike previous applications of ICD, time-critical vSLAM applications cannot assume an offline background modeling stage, or rely on maintenance-intensive background models. To address this issue, we introduce a novel maintenance-free ICD framework that requires no background modeling. We demonstrate that LCD can be reused as the main process for ICD with minimal extra cost. Based on these concepts, we develop a novel vSLAM component that enables simultaneous LCD and ICD. ICD experiments based on challenging cross-season LCD scenarios validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Pose tracking and geometry reconstruction are greatly significant for the high-level perceptual understanding and close-proximity operations of dynamic and geometrically unknown non-cooperative targets in space. However, the performance degrades severely under the commonly outlier-contaminated and corrupted measurements acquired from Time-of-Flight (ToF) cameras. In this paper, we are motivated to investigate the framework of both robust pose tracking and reliable geometry reconstruction. For the pose tracking, we propose an improved robust Iterative Closest Point (ICP) method based on adaptive Iteratively Reweighted Least Squares (IRLS), which can gradually jump out of the local minima in a naturally coarse-to-fine fashion. Besides, we put forward a hybrid feature-free loop closure detection approach to efficiently eliminate the accumulated error, meanwhile avoiding the ambiguity caused by the symmetrical structure. Regrading the geometry reconstruction, we present an explicit and general geometry uncertainty description, incorporated into a mixture-based probabilistic fusion method, to cope with the reconstruction defects. The experimental results show that our pose tracking and geometry reconstruction methods can achieve better performance in terms of robustness and accuracy. [Display omitted] • An adaptive IRLS-ICP method is proposed to boost the robustness of pose estimation. • A hybrid loop closure approach is proposed to avoid the geometry ambiguity. • A probabilistic fusion method integrated with a novel uncertainty model is presented. [ABSTRACT FROM AUTHOR]

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework's performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks. [ABSTRACT FROM AUTHOR]

SUMMARY: This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable. [ABSTRACT FROM AUTHOR]

Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n3) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM. [ABSTRACT FROM AUTHOR]

Most traditional loop closure detection (LCD) methods rely on manual feature design, which is sensitive to environmental conditions. Convolutional neural networks (CNNs) cope better with illumination changes by extracting hierarchical features and ignoring the local spatial characteristics of images. We propose an LCD algorithm that combines VGG16, NetVLAD, and image pyramids to enhance its accuracy and robustness. In particular, a three-level image pyramid was constructed via downsampling, and then a feature pyramid (FP) layer was obtained by extracting features through VGG16 network on different image resolutions. The obtained FPs were then passed into the VLAD model, and this model outputted VLAD vectors by performing residual summation with L2 normalization. Finally, a triplet loss function was employed for training. Experimental results on two benchmark datasets and a real scenario dataset demonstrated that this algorithm outperforms the NetVLAD baseline and the VGG16 network, exhibiting superior feature-learning capabilities and achieving a higher LCD accuracy. Further, it maintained real-time performance with only a 2% increase in processing time. The results indicate that the proposed method detects loop closures even in complex environments with varying conditions and perspectives. Hence, the approach can be used for large-scale visual simultaneous localization and mapping applications, such as autonomous driving, where LCD plays a crucial role in mapping. [ABSTRACT FROM AUTHOR]

Loop closure detection (LCD) is a challenging task to judge whether the current position of an intelligent robot returns to the previously visited position. Mainstream appearance-based approaches apply robust image representation techniques to describe the scene. However, most of these methods are designed for single images, and the sequence representation method incorporating temporal sequence information is still in the preliminary exploration. In this paper, we propose a compact sequence representation method for hierarchical LCD, ensuring conspicuous performance for the LCD task. Deriving from a group of global features of image sequences, we propose a multi-scale asymmetric temporal convolution network (MATC-Net), which generates sequential features and transformed global features through its aggregation branch and transformation branch, respectively. Based on these two types of features, a MATC-Net-based hierarchical LCD framework including two similarity measurement processes is constructed, through which the best place match is identified. The experimental results show that our method outperforms other counterparts on three datasets, exhibiting the promising potential of leveraging sequential features to LCD task. [Display omitted] • Propose a sequence representation method to utilize the temporal sequence information. • Design a multi-scale asymmetric temporal convolution network to generate sequential descriptors and transformed global descriptors. • Construct a hierarchical loop closure detection scheme identify the final loop event. [ABSTRACT FROM AUTHOR]

Reliable forest resource information is needed to assess the forest development status and design management plans for forest maintenance and conservation. The forest field sample inventory is a vital forest resource inventory method. Thus, forest inventory reliability depends on tree attribute estimation accuracy and the quantity and quality of field samples. Simultaneous localization and mapping (SLAM)-based mobile laser scanners (MLSs) are convenient inventory tools due to their mobility and global navigation satellite system (GNSS) signal independence. However, such scanners may be heavy, expensive and unable to verify results on-site. With the improved SLAM algorithm and chip computing capabilities, a smartphone can deploy an online SLAM system, which allows the smartphone to perform the relative positioning in forests without GNSS signals. Previous research studies have demonstrated this simple, portable, and economical device for estimating the tree position and diameter at breast height (DBH) through tree-by-tree measurements in real time. However, the device might face a challenge in large-scale forest inventories because the image-feature-based backend may not work well in forests that are not well constructed for traditional SLAM systems. In this paper, an online trunk-based backend was designed to accurately estimate tree position and correct pose drift in large-scale forest inventories in real time. Specifically, a trunk-based loop closure detection algorithm was designed for detecting whether an earlier observed tree is re-observed to provide nodes and constraints for tree position graph optimization; this algorithm uses the provided nodes and constraints to build and optimize the tree position graph and then correct the current pose based on the optimized globally consistent tree position graph. This new backend was integrated with the previous work as an executable program that can be deployed on a smartphone with an online RGB-D SLAM system. The method was tested in 5 field sample plots (32 × 32 m), and the reference tree positions were collected using terrestrial laser scanning (TLS) through multi-scan mode. The distance mean between the estimated and reference tree positions was 0.133 m when using our new backend, and it was 0.759 m when estimated with the RTAB-Map. The tree position estimates were unbiased and had root mean square errors (RMSEs) of less than 0.09 m in the x-axis, y-axis and z-axis directions when using the trunk-based backend. However, the estimates had biases of −0.125 m, −0.261 m and 0.262 m and RMSEs of more than 0.30 m in the x-axis, y-axis and z-axis directions without the new backend. The results showed that the designed trunk-based backend allows a smartphone with an online SLAM system to function as an accurate and efficient tool for large-scale forest inventories. However, the method was tested only in 32 × 32 m square field sample plots. More tests must be performed in larger plots in the future, although enough loop-closure constraints can theoretically guarantee the accuracy of the tree position graph and current pose. [ABSTRACT FROM AUTHOR]

How to improve the accuracy of loop detection is one of the key issues in the simultaneous positioning and map construction system to achieve higher pose recovery accuracy. Based on the traditional word bag model principle, this paper constructed a new algorithm framework. The algorithm used the pre· processed Faster-RCNN to detect the image sequence, and used the detected image semantic feature types, pixel positions and feature maps. To construct a two-dimensional semantic feature vector map with identification, and used nonlinear cumulative error to calculated the similarity between the two-dimensional semantic feature vector map, and calculated the initial loopback based on this, it obtained the final loop back result after pose verification. Compared with the traditional word bagging algorithm, the experimental results verify the effectiveness of the proposed algorithm and achieve more accurate and efficient loopback detection. [ABSTRACT FROM AUTHOR]

The firing of space cell in hippocampus is considered to be able to form an intrinsic map for the environment which is called cognitive map. Previous bionic navigation algorithms (such as rat simultaneous localization and mapping) and traditional SLAM algorithms lack sufficient physiological basis and cannot reflect the cognitive mechanism of hippocampal formation. Based on the cognitive mechanism of hippocampal space cells, this paper proposes a navigation algorithm for constructing accurate environmental cognitive map. This algorithm is characterized by the construction of a unified spatial cell attractor model for self-motion trajectory path integration. The expressions of grid cells and place cells are driven by stripe cells. The algorithm performs closed-loop detection by collecting depth image, red green blue+depthmap information and corrects the error of spatial cells’ path integral. Eventually, we get a precise cognitive map of the environment and bionic robot navigation is achieved by global navigation and local navigation algorithm based on the map. The cognitive map is a topological metric map that contains the topological relations of the environment feature point coordinates, visual cues, and specific sites. In this paper, the method is verified by the simulation experiment and the physical experiment on the robot platform. The research results laid the foundation for the research of the robot navigation method based on the hippocampus cognitive mechanism. Note to Practitioners—Environmental cognition and navigation ability are the main function of intelligent mobile robots. People and animals have strong adaptability and cognitive ability in unfamiliar environment, and they can independently recognize and navigate in unfamiliar environment. Now, the environmental cognition and navigation ability of intelligent robots are far from the level of human and animal. This paper suggests a bionic robot navigation algorithm based on the cognitive mechanism of rat hippocampus. The bionic robot navigation algorithm makes mobile robots more intelligent by learning and imitating human and animal’s environmental cognition and navigation ability. This navigation algorithm conforms to the physiological characteristics and is pretty accurate and intelligent. The experimental results demonstrate the effectiveness of this algorithm in building environment map and navigation. [ABSTRACT FROM AUTHOR]

Abstract A novel semi-direct monocular visual simultaneous localization and mapping (SLAM) system is proposed to maintain the fast performance of a direct method and the high precision and loop closure capability of a feature-based method. This system extracts and matches Oriented FAST and Rotated BRIEF features in a keyframe and tracks a non-keyframe via a direct method without the requirement of extracting and matching features. A keyframe is used for global or local optimization and loop closure, whereas a non-keyframe is used for fast tracking and localization, thereby combining the advantages of direct and feature-based methods. A monocular visual-inertial SLAM system that fuses inertial measurement data with visual SLAM is also proposed. This system successfully recovers the metric scale successfully. The evaluation on datasets shows that the proposed approach accomplishes loop closure detection successfully and requires less time to achieve accuracy comparable with that of feature-based method. The physical experiment demonstrates the feasibility and robustness of the proposed SLAM. The approach achieves good balance between speed and accuracy and provides valuable references for design and improvement of other SLAM methods. Highlights • A novel semi-direct monocular visual SLAM system is proposed. • The SLAM system also fuses inertial data to form a visual-inertial SLAM. • The method maintains the advantages of the direct and the feature-based method. • Comparative results of semi-direct SLAM are given. [ABSTRACT FROM AUTHOR]

The loop closure detection (LCD) problem in simultaneous localization and mapping has been an important research topic to reconstruct three-dimensional environments and estimate a camera trajectory accurately. Although the bag-of-visual-word (BoVW) scheme has been used widely and has shown good results, it has several problems in some aspects, such as occlusion, deformation, illumination change, and viewpoint change. In order to tackle the challenges, we propose an LCD method using the BoVW method with a local patch descriptor obtained from the learning-based approach. We have trained a neural network model with a place-oriented dataset and extract the descriptors for the local patches from the trained neural network model. In addition, we have constructed the ground-truth label for the evaluation. Our experiment shows promising results, compared to the state-of-the-art LCD method. The implementation of the proposed deep convolutional generative adversarial network descriptor, as well as the evaluation toolbox, can be found online at https://github.com/JustWon/DCGAN%5FDescriptor. [ABSTRACT FROM AUTHOR]

Loop closure detection (LCD) is essential in the field of visual Simultaneous Localization and Mapping (vSLAM). In the LCD system, geometrical verification based on image matching plays a crucial role in avoiding erroneous detections. This paper focuses on adopting patch-level local features for image matching to compute the similarity score between the current query image and the candidate images. However, an important factor that may reduce the robustness is that some distracting and dynamic regions in a scene (e.g., the sky, cars, pedestrians, the ground, etc.) are not helpful and may seriously harm the performance. To address this challenge, we first use a newly designed patch descriptor loss to optimize the distance relationship between the patch-level local features. In this way, the patch-level local features extracted from the query/candidate images are more suitable for performing image matching. Moreover, we mimic the visual attention mechanism and propose a patch matching with saliency strategy, which enables local patches in salient regions to play crucial roles in image matching by assigning suitable weights to them. Finally, experiments on several public datasets demonstrate that the proposed LCD system can achieve encouraging improvements over the state-of-the-art approaches regarding recall rates under 100% precision. • Create a novel LCD-oriented saliency prediction dataset (Saliency-LCD). • Design SaliencyNetVLAD to extract patch-level local features and global features. • Patch-level local features are optimized by using the novel patch descriptor loss. • Use the predicted saliency map to improve the geometrical verification process. [ABSTRACT FROM AUTHOR]

• A method based on the fusion of the local and global descriptors is proposed. • A coarse-to-fine hierarchical recognition strategy is designed. • The proposed method is loosely coupled with the SLAM framework. • A comprehensive evaluation is conducted on datasets of different scales. • It is shown that a good initial value can provide point cloud registration. Place recognition, which is also known as loop detection, has been one of the main problems in the field of robot navigation, and it is crucial in realizing high-performance simultaneous localization and mapping (SLAM). Most of the existing methods directly construct local or global descriptors with point clouds, ignoring the offset of different visits in the same place. To overcome the current limitations, this study emphasizes the importance of point cloud features in improving the environment recognition precision and proposes an innovative framework that combines local features that are spatially invariant and prominent descriptive global features as a representation of a laser scanner frame. The feature center of the laser point cloud is obtained by computing local feature points with spatial invariance, which improves the offset between the original point cloud and the feature center. Furthermore, a coarse-to-fine hierarchical recognition strategy is developed to improve the efficiency of place recognition. An evaluation with different offsets and synthesis is conducted on the publicly available KITTI and self-developed CHDloop datasets. The experimental results show that the proposed method can improve time efficiency by more than 18% compared to the SC and ISC, and there are also significant improvements in the recall rate and accuracy in slight lane-change scenarios. Moreover, the proposed method can obtain rich lane-change loops in moderate and large lane-change scenarios where the SC and ISC fail to obtain effective loops. The proposed method can be used as a lightweight and interpretable approach for place recognition, which not only can be quickly deployed in any SLAM framework but can also efficiently handle more complex place recognition issues in urban environments. [ABSTRACT FROM AUTHOR]

In the loop closure detection of 2D laser SLAM, there is a problem that tlie number of matching candidates is large and prone to false detection, this paper proposed a loop closure detection metliod based on submap matching. In this method， several consecutive frames of laser range data used to construct tlie submap and the constructed submap generated the compression table. Then，combined with the pose estimation of the robot，the low precision scan matching matched with the compression table，and selected the matching candidate set of loop closure detection to improve the matching speed. Finally，the high precision scan matching used the selected matching candidate set to detect loop closure，to meet the require ments of the speed and accuracy of the loop closure detection of the SLAM under the large-scale map. The experimental results show that the algorithm can effectively improve the reliability of loop closure detection and significantly reduces the number of matching candi- date sets. [ABSTRACT FROM AUTHOR]

Illumination variations result distant image representations of the same places, posing difficulty in image-based place recognition. We propose a shape-based global image descriptor and a matching method to address the problem. To describe a local image patch, a set of robust edge pixels is selected by thresholding a small proportion of pixels with large edge responses and represented by a shape descriptor which captures the local geometric features instead of image intensities. To alleviate the unbalanced distribution of edge pixels, the whole image is partitioned into regular patches and represented by a collection of local descriptors from individual patches. Instead of including the dissimilarity measurements of all patches in comparing two images, we propose to sum up a fraction of small measurements as a way to reject noisy patches. Experiments show that our global image descriptor supported by the proposed matching method achieves the state-of-the-art performance for visual place recognition. [ABSTRACT FROM AUTHOR]

In this paper, we look into the problem of loop closure detection in topological mapping. The bag of words (BoW) is a popular approach which is fast and easy to implement, but suffers from perceptual aliasing, primarily due to vector quantization. We propose to overcome this limitation by incorporating the spatial co-occurrence information directly into the dictionary itself. This is done by creating an additional dictionary comprising of word pairs, which are formed by using a spatial neighborhood defined based on the scale size of each point feature. Since the word pairs are defined relative to the spatial location of each point feature, they exhibit a directional attribute which is a new finding made in this paper. The proposed approach, called bag of word pairs (BoWP), uses relative spatial co-occurrence of words to overcome the limitations of the conventional BoW methods. Unlike previous methods that use spatial arrangement only as a verification step, the proposed method incorporates spatial information directly into the detection level and thus, influences all stages of decision making. The proposed BoWP method is implemented in an on-line fashion by incorporating some of the popular concepts such as, K-D tree for storing and searching features, Bayesian probabilistic framework for making decisions on loop closures, incremental creation of dictionary and using RANSAC for confirming loop closure for the top candidate. Unlike previous methods, an incremental version of K-D tree implementation is used which prevents rebuilding of tree for every incoming image, thereby reducing the per image computation time considerably. Through experiments on standard datasets it is shown that the proposed methods provide better recall performance than most of the existing methods. This improvement is achieved without making use any geometric information obtained from range sensors or robot odometry. The computational requirements for the algorithm is comparable to that of BoW methods and is shown to be less than the latest state-of-the-art method in this category. [ABSTRACT FROM AUTHOR]

Visual-inertial odometry (VIO) shows high localization accuracy in many environments. However, the inevitable estimation drift is constantly accumulating, which even leads to failures. Hence, an efficient loop closing (LC) is necessary for correcting drift. Unlike most existing methods rely on pre-trained model, we propose an incremental hierarchical forest (HF) based LC method (HF-LC), which online encodes frames for loop closing without any prior information, and it is suitable for arbitrary images. We thoroughly exploit the duality of binary descriptors, and propose a hierarchical forest based descriptor search method for loop frame retrieval, which is extremely fast with efficient descriptor clustering, binary search, and grid based loop closure selection modules. Moreover, we integrate HF-LC into a state-of-the-art keyframe based VIO, and develop a comprehensive hierarchical forest based visual-inertial SLAM (HFVIS) system. Furthermore, a lightweight geometric verification is designed for correctness checking and pose computing of loop keyframes. Finally, all keyframe poses are further optimized in global pose-graph optimization, which achieves better consistency. The efficacy of our method is validated on ground robot and aerial vehicle datasets. Extensive results confirm that the proposed HF-LC achieves better precision–recall performance, and our HFVIS system has higher localization accuracy and superior real-time performance. • An incremental hierarchical forest based binary descriptor search method is proposed, which has low computational complexity. • An online loop keyframe retrieval algorithm is proposed, which is extremely fast with better precision-recall performance. • A comprehensive hierarchical forest based visual-inertial SLAM system is developed. • Extensive results verify the superiority in both localization accuracy and real-time performance. [ABSTRACT FROM AUTHOR]

Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping (SLAM) systems, making it significant for eliminating accumulative trajectory error and constructing a consistent map. Focusing on visual SLAM, we propose a LCD method using local-global similarity measurement strategies. As with most deep learning-based methods, we treat the LCD problem as a classification problem. We use a pretrained VGG-16 network to obtain advanced features and a multiscale feature extraction module to integrate the features of different receptive fields. Finally, we overcome the problems of dynamic object occlusion and large-area repetitive texture using a local-global similarity measurement module to measure the similarity between current and historical frames in all dimensions. This approach decouples feature extraction from classification determination, which enables us to use the advantages of both supervised and unsupervised learning to cope with the imbalance in the number of nonloop and loop pairs. Experimental results on three different datasets showed that the proposed method is significantly better than the state-of-the-art appearance-based LCD methods in dynamic urban, suburban, and highway scenarios. [ABSTRACT FROM AUTHOR]

Closed loop detection can alleviate the error accumulation during the operation of Visual Simultaneous Localization and Mapping (VSLAM) system, which is of great significance to the accuracy and robustness of the robot. Triplet loss based metric learning has been proposed for closed loop detection in this paper. Firstly, a triplet selection strategy has been constructed. The Softplus function is applied to triplet loss so that the loss in the negative axis will be soft margin. The adaptive margin has been proposed to maintain the mapping distribution in metric space. Metric learning converts keyframes into feature vectors, evaluating the similarity of keyframes by calculating the Euclidean distance between feature vectors, which is utilized to determine whether a closed loop is formed. Secondly, detection strategy of candidate keyframes for loop detection is introduced according to Euclidean distance. Finally, triplet loss based metric learning is applied to closed loop detection for VSLAM system. VSLAM datasets have been applied to evaluate the precision and recall of metric learning model, and then the established VSLAM system has been implemented in real-time application. The experimental results illustrate the feasibility and effectiveness of proposed method, which can be further applied to practical VSLAM systems. • A triplet selection strategy has been constructed to speed up the convergence. • The Softplus is applied so that the loss in the negative axis will be soft margin. • The margin constructed for different classes has been proposed. • Metric learning has been proposed for closed loop detection. [ABSTRACT FROM AUTHOR]

The automatic place recognition problem is one of the key challenges in SLAM approaches for loop closure detection. Most of the appearance-based solutions to this problem share the idea of image feature extraction, memorization, and matching search. The weakness of these solutions is the storage and computational costs which increase drastically with the environment size. In this regard, the major constraints to overcome are the required visual information storage and the complexity of similarity computation. In this paper, a novel formulation is proposed that allows the computation time reduction while no visual information are stored and matched explicitly. The proposed solution relies on the incremental building of a bio-inspired visual memory using a Fuzzy ART network. This network considers the properties discovered in primate brain. The performance evaluation of the proposed method has been conducted using two datasets representing different large scale outdoor environments. The method has been compared with RatSLAM and FAB-MAP approaches and has demonstrated a decreased time and storage costs with broadly comparable precision recall performance. [ABSTRACT FROM AUTHOR]

Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic–visual–geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference. [ABSTRACT FROM AUTHOR]

Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author. [ABSTRACT FROM AUTHOR]

Robust loop-closure detection is essential for visual SLAM. Traditional methods often focus on the geometric and visual features in most scenes but ignore the semantic information provided by objects. Based on this consideration, we present a strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection. To align two sub-graphs efficiently, we use a sparse Kuhn–Munkres algorithm to speed up the search for correspondence among nodes. The shape similarity and the Euclidean distance between objects in the 3-D space are leveraged unitedly to measure the image similarity through graph matching. Furthermore, the proposed approach has been analyzed and compared with the state-of-the-art algorithms at several datasets as well as two indoor real scenes, where the results indicate that our semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision. [ABSTRACT FROM AUTHOR]

Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot's estimated pose and generate a consistent global map. Many variations of this problem have been considered in the past and the existing methods differ in the acquisition approach of query and reference views, the choice of scene representation, and associated matching strategy. Contributions of this survey are many-fold. It provides a thorough study of existing literature on loop closure detection algorithms for visual and Lidar SLAM and discusses their insight along with their limitations. It presents a taxonomy of state-of-the-art deep learning-based loop detection algorithms with detailed comparison metrics. Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. Furthermore, open challenges and future directions were also discussed. [ABSTRACT FROM AUTHOR]

A combined method involving global and local descriptors was developed to recognise scenes for loop closure detection in low‐textured environments. An omnidirectional image is divided into background regions and salient regions according to the colour distribution. To represent a scene with features that are appropriate to its characteristics, global features for background regions are calculated and scale invariant feature transform features for salient regions are extracted. The proposed method can compute a more distinct scene similarity, and this was verified by an experiment involving loop closure detection. [ABSTRACT FROM AUTHOR]

Loop closure detection is a key module for visual simultaneous localization and mapping (SLAM). Most previous methods for this module have not made full use of the information provided by images, i.e., they have only used the visual appearance or have only considered the spatial relationships of landmarks; the visual, spatial and semantic information have not been fully integrated. In this paper, a robust loop closure detection approach integrating visual–spatial–semantic information is proposed by employing topological graphs and convolutional neural network (CNN) features. Firstly, to reduce mismatches under different viewpoints, semantic topological graphs are introduced to encode the spatial relationships of landmarks, and random walk descriptors are employed to characterize the topological graphs for graph matching. Secondly, dynamic landmarks are eliminated by using semantic information, and distinctive landmarks are selected for loop closure detection, thus alleviating the impact of dynamic scenes. Finally, to ease the effect of appearance changes, the appearance-invariant descriptor of the landmark region is extracted by a pre-trained CNN without the specially designed manual features. The proposed approach weakens the influence of viewpoint changes and dynamic scenes, and extensive experiments conducted on open datasets and a mobile robot demonstrated that the proposed method has more satisfactory performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The recognition of previously visited places, known as Loop Closure Detection (LCD), composes one of the problems widely studied in robotics: simultaneous localization and mapping (SLAM). In this paper we propose a three level hierarchy based LCD method. In our serialized approach, in the First Level, a sequence of the most recently visited places is used as query to search for candidate sequences in our topological map composed by previously visited places. After that, at the Second Level, the method selects the most similar sequence to the query among all candidate sequences which is temporally consistent with the previous LCD method response. Then, at the Third Level, we match the image sequences belonging to the query sequence to the candidate sequence selected in the Second Level. The method is evaluated in different and challenging public datasets, and presents expressive results that overcome the LCD state-of-the-art methods. • The Three Level Sequence-Based Loop Closure Detection is a visual-based LCD method. • The novel approach overcomes other works in the recall at 100% precision. • By grouping images into places, we reduce the amount of information to be processed. • The idea of places highlights the distinct points of a traversal. • RST-SHelo descriptor helps our method to be more robust to changes in point of view. [ABSTRACT FROM AUTHOR]

Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on "Simplified Structure". Firstly, we extract the "Simplified Structure" from the indoor point cloud, classify them into two levels, and manage the "Simplified Structure" hierarchically according to its structure salience. The "Simplified Structure" has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with "Simplified Structure" when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively. [ABSTRACT FROM AUTHOR]

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality. [ABSTRACT FROM AUTHOR]

The detection of true loop closure in Visual Simultaneous Localization And Mapping (vSLAM) can help in many ways, it helps in re-localization, improves the accuracy of the map, and helps in registration algorithms to obtain more accurate and consistent results. The loop closure detection is affected by many parameters, including illumination conditions, seasons, different viewpoints and mobile objects. This paper proposes a novel approach based on super dictionary different from traditional BoW dictionary that uses more advanced and more abstract features of deep learning. The proposed approach does not need to generate vocabulary, which makes it memory efficient and instead it stores exact features, which are small in number and hold very less amount of memory as compared to traditional BoW approach in which each frame holds the same amount of memory as the number of words in the vocabulary. Two deep neural networks are used together to speed up the loop closure detection and to ignore the effect of mobile objects on loop closure detection. We have compared the results with most popular Bag of Words methods DBoW2 and DBoW3, and state-of-the-art iBoW-LCD using five publicly available datasets, and the results show that the proposed method robustly performs loop closure detection and is eight times faster than the state-of-the-art approaches of a similar kind. [ABSTRACT FROM AUTHOR]

This paper presents an offline mapping algorithm for autonomous vehicles (AV) with low-cost sensors. The mapping algorithm consists of five key steps. First, data pre-processing is conducted to calibrate the original odometry data. Then, based on a 2D laser scanner and the calibrated odometry data, a virtual 3D light detection and ranging (LiDAR) is built. In the third step, loop closure is performed to search the revisited region and calculate the distance displacement. Afterward, the optimizer is applied to generate the final trajectory. Finally, by fusing the point cloud data from virtual 3D LiDAR and the final trajectory, the point cloud map is generated. Field experiments are conducted in both open and urban areas. In these two cases, satisfactory point cloud maps are built even the vehicle travels a long distance with loop closure, and the constructed point cloud map can be used for AV localization. [ABSTRACT FROM AUTHOR]