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In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based tasks. Today, however, robotics and AI are moving into the wild, where human–robot collaboration and robust operation are essential. One of the most demanding scenarios involves deploying autonomous drones in GNSS-denied environments, such as dense forests. Despite the challenges, the potential to exploit natural resources in these settings underscores the importance of developing technologies that can operate in such conditions. In this study, we present a methodology that addresses the unique challenges of natural forest environments by integrating positioning methods, leveraging cameras, LiDARs, GNSS, and vision AI with drone technology for under-canopy wild berry mapping. To ensure practical utility for fruit harvesters, we generate intuitive heat maps of berry locations and provide users with a mobile app that supports interactive map visualization, real-time positioning, and path planning assistance. Our approach, tested in a Scandinavian forest, refines the identification of high-yield wild fruit locations using V-SLAM, demonstrating the feasibility and effectiveness of autonomous drones in these demanding applications.

In this work, an innovative perception-guided approach is proposed for landing zone detection and realization of Unmanned Aerial Vehicles (UAVs) operating in unstructured environments ridden with obstacles. To accommodate secure landing, two well-established tools, namely fuzzy systems and visual Simultaneous Localization and Mapping (vSLAM), are implemented into the landing pipeline. Firstly, colored images and point clouds acquired by a visual sensory device are processed to serve as characterizing maps that acquire information about flatness, steepness, inclination, and depth variation. By leveraging these images, a novel fuzzy map infers the areas for risk-free landing on which the UAV can safely land. Subsequently, the vSLAM system is employed to estimate the platform’s pose and an additional set of point clouds. The vSLAM point clouds presented in the corresponding keyframe are projected back onto the image plane on which a threshold fuzzy landing score map is applied. In other words, this binary image serves as a mask for the re-projected vSLAM world points to identify the best subset for landing. Once these image points are identified, their corresponding world points are located, and among them, the center of the cluster with the largest area is chosen as the point to land. Depending on the UAV’s size, four synthesis points are added to the vSLAM point cloud to execute the image-based visual servoing landing using image moment features. The effectiveness of the landing package is assessed through the ROS Gazebo simulation environment, where comparisons are made with a state-of-the-art landing site detection method.

Visual–Inertial SLAM (VI-SLAM) has a wide range of applications spanning robotics, autonomous driving, AR, and VR due to its low-cost and high-precision characteristics. VI-SLAM is divided into localization and mapping tasks. However, researchers focus more on the localization task while the robustness of the mapping task is often ignored. To address this, we propose a map-point convergence strategy which explicitly estimates the position, the uncertainty, and the stability of the map point (SoM). As a result, the proposed method can effectively improve the quality of the whole map while ensuring state-of-the-art localization accuracy. The convergence strategy mainly consists of a perpendicular-based triangulation and 3D Gaussian–uniform mixture filter, and we name it PGMF, perpendicular-based 3D Gaussian–uniform mixture filter. The algorithm is extensively tested on open-source datasets, which shows the RVM (Ratio of Valid Map points) of our algorithm exhibits an average increase of 0.1471 compared to VINS-mono, with a variance reduction of 68.8%.

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes.

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.

The space environment is characterized by unstructured features, sparsity, and poor lighting conditions. The difficulty in extracting features from the visual frontend of traditional SLAM methods results in poor localization and time-consuming issues. This paper proposes a rapid and real-time localization and mapping method for star chart surveyors in unstructured space environments. Improved localization is achieved using multiple sensor fusion to sense the space environment. We replaced the traditional feature extraction module with an enhanced SuperPoint feature extraction network to tackle the challenge of challenging feature extraction in unstructured space environments. By dynamically adjusting detection thresholds, we achieved uniform detection and description of image keypoints, ultimately resulting in robust and accurate feature association information. Furthermore, we minimized redundant information to achieve precise positioning with high efficiency and low power consumption. We established a star surface rover simulation system and created simulated environments resembling Mars and the lunar surface. Compared to the LVI-SAM system, our method achieved a 20% improvement in localization accuracy for lunar scenarios. In Mars scenarios, our method achieved a positioning accuracy of 0.716 m and reduced runtime by 18.682 s for the same tasks. Our approach exhibits higher localization accuracy and lower power consumption in unstructured space environments.

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition.

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH>20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used.

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety–distance–error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety–distance–error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms.

The simultaneous localization and mapping (SLAM) technique provides long-term near-seafloor navigation for autonomous underwater vehicles (AUVs). However, the stability of the interest point detector (IPD) remains challenging in the seafloor environment. This paper proposes an optical–acoustic fusion interest point detector (OAF-IPD) using a monocular camera and forward-looking sonar. Unlike the artificial feature detectors most underwater IPDs adopt, a deep neural network model based on unsupervised interest point detector (UnsuperPoint) was built to reach stronger environmental adaption. First, a feature fusion module based on feature pyramid networks (FPNs) and a depth module were integrated into the system to ensure a uniform distribution of interest points in depth for improved localization accuracy. Second, a self-supervised training procedure was developed to adapt the OAF-IPD for unsupervised training. This procedure included an auto-encoder framework for the sonar data encoder, a ground truth depth generation framework for the depth module, and optical–acoustic mutual supervision for the fuse module training. Third, a non-rigid feature filter was implemented in the camera data encoder to mitigate the interference from non-rigid structural objects, such as smoke emitted from active vents in hydrothermal areas. Evaluations were conducted using open-source datasets as well as a dataset captured by the research team of this paper from pool experiments to prove the robustness and accuracy of the newly proposed method.

SLAM (simultaneous localization and mapping) is essential for accurate positioning and reasonable path planning in outdoor mobile robots. LiDAR SLAM is currently the dominant method for creating outdoor environment maps. However, the mainstream LiDAR SLAM algorithms have a single point cloud feature extraction process at the front end, and most of the loop closure detection at the back end is based on RNN (radius nearest neighbor). This results in low mapping accuracy and poor real-time performance. To solve this problem, we integrated the functions of point cloud segmentation and Scan Context loop closure detection based on the advanced LiDAR-inertial SLAM algorithm (LIO-SAM). First, we employed range images to extract ground points from raw LiDAR data, followed by the BFS (breadth-first search) algorithm to cluster non-ground points and downsample outliers. Then, we calculated the curvature to extract planar points from ground points and corner points from clustered segmented non-ground points. Finally, we used the Scan Context method for loop closure detection to improve back-end mapping speed and reduce odometry drift. Experimental validation with the KITTI dataset verified the advantages of the proposed method, and combined with Walking, Park, and other datasets comprehensively verified that the proposed method had good accuracy and real-time performance.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform paths characterizes this error. In response to this challenge, we propose a linear, parameter-free model that uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established by the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit’s size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model’s effectiveness in 3D pose estimation, with substantial pose differences and efficient pose optimization in larger circuits.

With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems without overlapping parts is inaccurate. Furthermore, the potential of feature matching points and their spatial extent in calculating the extrinsic parameters of multi-camera systems has not yet been fully realized. To this end, we propose a multi-camera calibration algorithm to solve the problem of the high-precision calibration of multi-camera systems without overlapping parts. The calibration of multi-camera systems is simplified to the problem of solving the transformation relationship of extrinsic parameters using a map constructed by multiple cameras. Firstly, the calibration environment map is constructed by running the SLAM algorithm separately for each camera in the multi-camera system in closed-loop motion. Secondly, uniformly distributed matching points are selected among the similar feature points between the maps. Then, these matching points are used to solve the transformation relationship between the multi-camera external parameters. Finally, the reprojection error is minimized to optimize the extrinsic parameter transformation relationship. We conduct comprehensive experiments in multiple scenarios and provide results of the extrinsic parameters for multiple cameras. The results demonstrate that the proposed method accurately calibrates the extrinsic parameters for multiple cameras, even under conditions where the main camera and auxiliary cameras rotate 180°.

In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas.

LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods.

Underwater terrain-aided navigation (TAN) can obtain high-precision positioning independently and autonomously under the conditions of a communication rejection space, which is an important breakthrough for the autonomous and refined operation of deep-sea autonomous underwater vehicles near the seabed. Although TAN originated in the aviation field, the particularity of the underwater physical environment has led to the formation of a different theoretical and technical system. In this article, the application background, operating principles, and most important technical aspects of underwater TAN are introduced. Then, the relevant algorithms involved in the two main modules (the terrain-aided positioning module and the iterative filtering estimation module) of the underwater TAN are reviewed. Finally, other cutting-edge issues in the field of underwater TAN are summarized. The purpose of this article is to provide researchers with a comprehensive understanding of the current research status and possible future developments in the TAN field.

The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite’s position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method’s potential for practical applications.

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.

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.

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos. Despite the wide range of applications for SfM, performing mainstream SfM methods on such videos poses challenges due to their high computational cost. Our method consists of three main steps. Firstly, we employ a visual SLAM (VSLAM) system to efficiently extract keyframes, keypoints, initial camera poses, and sparse structures from downsampled videos. Next, we propose a novel two-step keypoint adjustment method. Instead of matching new points in the original videos, our method effectively and efficiently adjusts the existing keypoints at the original scale. Finally, we refine the poses and structures using a rotation-averaging constrained global bundle adjustment (BA) technique, incorporating the adjusted keypoints. To enrich the resources available for SLAM or SfM studies, we provide a large-size (3840 × 2160) outdoor video dataset with millimeter-level-accuracy ground control points, which supplements the current relatively low-resolution video datasets. Experiments demonstrate that, compared with other SLAM or SfM methods, our method achieves an average efficiency improvement of 100% on our collected dataset and 45% on the EuRoc dataset. Our method also demonstrates superior localization accuracy when compared with state-of-the-art SLAM or SfM methods.

Multi-sensor fusion is pivotal in augmenting the robustness and precision of simultaneous localization and mapping (SLAM) systems. The LiDAR–visual–inertial approach has been empirically shown to adeptly amalgamate the benefits of these sensors for SLAM across various scenarios. Furthermore, methods of panoptic segmentation have been introduced to deliver pixel-level semantic and instance segmentation data in a single instance. This paper delves deeper into these methodologies, introducing PSMD-SLAM, a novel panoptic segmentation assisted multi-sensor fusion SLAM approach tailored for dynamic environments. Our approach employs both probability propagation-based and PCA-based clustering techniques, supplemented by panoptic segmentation. This is utilized for dynamic object detection and the removal of visual and LiDAR data, respectively. Furthermore, we introduce a module designed for the robust real-time estimation of the 6D pose of dynamic objects. We test our approach on a publicly available dataset and show that PSMD-SLAM outperforms other SLAM algorithms in terms of accuracy and robustness, especially in dynamic environments.

With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness.

Fast and accurate pose estimation is essential for the local motion control of robots such as drones. At present, camera-based motion capture (Mocap) systems are mostly used by robots. However, this kind of Mocap system is easily affected by light noise and camera occlusion, and the cost of common commercial Mocap systems is high. To address these challenges, we propose Easy Rocap, a low-cost, open-source robot motion capture system, which can quickly and robustly capture the accurate position and orientation of the robot. Firstly, based on training a real-time object detector, an object-filtering algorithm using class and confidence is designed to eliminate false detections. Secondly, multiple-object tracking (MOT) is applied to maintain the continuity of the trajectories, and the epipolar constraint is applied to multi-view correspondences. Finally, the calibrated multi-view cameras are used to calculate the 3D coordinates of the markers and effectively estimate the 3D pose of the target robot. Our system takes in real-time multi-camera data streams, making it easy to integrate into the robot system. In the simulation scenario experiment, the average position estimation error of the method is less than 0.008 m, and the average orientation error is less than 0.65 degrees. In the real scenario experiment, we compared the localization results of our method with the advanced LiDAR-Inertial Simultaneous Localization and Mapping (SLAM) algorithm. According to the experimental results, SLAM generates drifts during turns, while our method can overcome the drifts and accumulated errors of SLAM, making the trajectory more stable and accurate. In addition, the pose estimation speed of our system can reach 30 Hz.

This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument.

Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

Chestnut and chestnut byproducts are of worldwide interest, so there is a constant need to develop faster and more accurate monitoring techniques. Recent advances in simultaneous localization and mapping (SLAM) algorithms and user accessibility have led to increased use of handheld mobile laser scanning (HHLS) in precision agriculture. We propose a tree growth monitoring methodology, based on HHLS point cloud processing, that calculates the length of branches through spatial discretization of the point cloud for each tree. The methodology was tested by comparing two point clouds collected almost simultaneously for each of a set of sweet chestnut trees. The results obtained indicated that our HHLS method was reliable and accurate in efficiently monitoring sweet chestnut tree growth. The same methodology was used to calculate the growth of the same set of trees over 37 weeks (from spring to winter). Differences in week 0 and week 37 scans showed an approximate mean growth of 0.22 m, with a standard deviation of around 0.16 m reflecting heterogeneous tree growth.

This paper presents an innovative dataset designed explicitly for challenging agricultural environments, such as greenhouses, where precise location is crucial, but GNNS accuracy may be compromised by construction elements and the crop. The dataset was collected using a mobile platform equipped with a set of sensors typically used in mobile robots as it was moved through all the corridors of a typical Mediterranean greenhouse featuring tomato crops. This dataset presents a unique opportunity for constructing detailed 3D models of plants in such indoor-like spaces, with potential applications such as robotized spraying. For the first time, to the authors’ knowledge, a dataset suitable to test simultaneous localization and mapping (SLAM) methods is presented in a greenhouse environment, which poses unique challenges. The suitability of the dataset for this purpose is assessed by presenting SLAM results with state-of-the-art algorithms. The dataset is available online.

Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity, and history of a people. They also contribute to the ecological balance of the city. Despite the fact that gardens have an historic and social value, they are not protected as much as the rest of the existing heritage, such as architecture and archaeological sites. While methods of built-heritage mapping and monitoring are increasing and constantly improving to reduce built-heritage loss and the severe impact of natural disasters, the documentation and survey techniques for gardens are often antiquated. In addition, inventories are typically made by non-updated/updateable reports, and they are rarely in digital format or in 3D. This paper presents the results of a comprehensive study on the latest technology for laser scanning in gardens. We compared static terrestrial laser scanning and mobile laser scanning point clouds generated by the Focus 3D S120 and the Leica BLK2GO, respectively, to evaluate their quality for documentation, estimate tree attributes, and terrain morphology. The evaluation is based on visual observation, C2C comparisons, and terrain information extraction capabilities, i.e., M3C2 comparisons for topography, DTM generation, and contour lines. Both methods produced useful outcomes for the scope of the research within their limitations. Terrestrial laser scanning is still the method that offers accurate point clouds with a higher point density and less noise. However, the more recent mobile laser scanning is able to survey in less time, significantly reducing the costs for site activities, data post-production, and registration. Both methods have their own restrictions that are amplified by site features, mainly the lack of plans for the geometric alignment of scans and the simultaneous location and mapping (SLAM) process. We offer a critical description of the issues related to the functionality of the two sensors, such as the operative range limit, light dependency, scanning time, point cloud completeness and size, and noise level.

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. This paper firstly analyzes the current situation of NeRF and SLAM systems and then introduces the state-of-the-art in NeRF-based SLAM. In addition, datasets and system evaluation methods used by NeRF-based SLAM are introduced. In the end, current issues and future work are analyzed. Based on an investigation of 30 related research articles, this paper provides in-depth insight into the innovation of SLAM and NeRF methods and provides a useful reference for future research.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

Outdoor Location-Based Augmented Reality (LAR) applications require precise positioning for seamless integrations of virtual content into immersive experiences. However, common solutions in outdoor LAR applications rely on traditional smartphone sensor fusion methods, such as the Global Positioning System (GPS) and compasses, which often lack the accuracy needed for precise AR content alignments. In this paper, we introduce an innovative approach to enhance LAR anchor precision in outdoor environments. We leveraged Visual Simultaneous Localization and Mapping (VSLAM) technology, in combination with innovative cloud-based methodologies, and harnessed the extensive visual reference database of Google Street View (GSV), to address the accuracy limitation problems. For the evaluation, 10 Point of Interest (POI) locations were used as anchor point coordinates in the experiments. We compared the accuracies between our approach and the common sensor fusion LAR solution comprehensively involving accuracy benchmarking and running load performance testing. The results demonstrate substantial enhancements in overall positioning accuracies compared to conventional GPS-based approaches for aligning AR anchor content in the real world.

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

This paper explores the research topic of enhancing the reliability of unmanned mobile exploration using LiDAR SLAM. Specifically, it proposes a technique to analyze waypoints where 3D LiDAR SLAM can be smoothly performed in potential exploration areas and points where there is a risk of divergence in navigation estimation. The goal is to improve exploration performance by presenting a method that secures these candidate regions. The analysis employs a 3D geometric observability matrix and its condition number to discriminate waypoints. Subsequently, the discriminated values are applied to path planning, resulting in the derivation of a final destination path connecting waypoints with a satisfactory SLAM position and attitude estimation performance. To validate the proposed technique, performance analysis was initially conducted using the Gazebo simulator. Additionally, experiments were performed with an autonomous unmanned vehicle in a real-world environment.

In this study, we evaluated the performance of GeoSLAM ZEB Horizon and Stonex X120GO SLAM handheld mobile laser scanners (HMLS) to address two primary objectives. First, we aimed to assess and compare the accuracy of positioning achieved using HMLS instruments. Second, we sought to investigate the influencing factors and their impact on estimation accuracies. The factors influencing the accuracy of positioning in HMLS Simultaneous Localization and Mapping-aided solutions were defined, considering the scanner type, distance from the trajectory, forest structure, tree species, and Diameter at Breast Height. The same type of trajectory was tested in five different stand structures. The evaluation of GeoSLAM HMLS point clouds yielded an average positional RMSE of 17.91 cm, while the data extracted from the Stonex HMLS resulted in an average positional RMSE of 17.33 cm. These results underscore the significant potential of HMLS technology in addressing the critical need for precise positioning data in various applications, from forestry management to environmental monitoring, wildlife habitat assessment, and climate change studies. By harnessing the power of handheld mobile laser scanners, our research aims to enhance the accuracy and efficiency of geospatial data capture in challenging.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.

Micro-hexapods, well-suited for navigating tight or uneven spaces and suitable for mass production, hold promise for exploration by robot groups, particularly in disaster scenarios. However, research on simultaneous localization and mapping (SLAM) for micro-hexapods has been lacking. Previous studies have not adequately addressed the development of SLAM systems considering changes in the body axis, and there is a lack of comparative evaluation with other movement mechanisms. This study aims to assess the influence of walking on SLAM capabilities in hexapod robots. Experiments were conducted using the same SLAM system and LiDAR on both a hexapod robot and crawler robot. The study compares map accuracy and LiDAR point cloud data through pattern matching. The experimental results reveal significant fluctuations in LiDAR point cloud data in hexapod robots due to changes in the body axis, leading to a decrease in map accuracy. In the future, the development of SLAM systems considering body axis changes is expected to be crucial for multi-legged robots like micro-hexapods. Therefore, we propose the implementation of a system that incorporates body axis changes during locomotion using inertial measurement units and similar sensors.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This paper focuses on a visual SLAM method specifically designed for complex dynamic environments. Our approach proposes a dynamic feature removal module based on the tight coupling of instance segmentation and multi-view geometric constraints (TSG). This method seamlessly integrates semantic information with geometric constraint data, using the fundamental matrix as a connecting element. In particular, instance segmentation is performed on frames to eliminate all dynamic and potentially dynamic features, retaining only reliable static features for sequential feature matching and acquiring a dependable fundamental matrix. Subsequently, based on this matrix, true dynamic features are identified and removed by capitalizing on multi-view geometry constraints while preserving reliable static features for further tracking and mapping. An instance-level semantic map of the global scenario is constructed to enhance the perception and understanding of complex dynamic environments. The proposed method is assessed on TUM datasets and in real-world scenarios, demonstrating that TSG-SLAM exhibits superior performance in detecting and eliminating dynamic feature points and obtains good localization accuracy in dynamic environments.

Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system.

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.