Search

Simultaneous localization and mapping (SLAM) are essential in numerous robotics applications, such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric mapof the environment. While existing algorithms exhibit good results, they are still sensitive to measurement noise,sensor quality, and data association and are still computationally expensive. Alternatively, some navigation andmapping missions can be achieved using only qualitative geometric information, an approach known as qualitativespatial reasoning (QSR). We contribute a novel probabilistic qualitative localization and mapping approach in thiswork. We infer both the qualitative map and the qualitative state of the camera poses (localization). For the firsttime, we also incorporate qualitative probabilistic constraints between camera poses (motion model), improvingcomputation time and performance. Furthermore, we take advantage of qualitative inference properties to achievevery fast approximated algorithms with good performance. In addition, we show how to propagate probabilisticinformation between nodes in the qualitative map, which improves estimation performance and enables inferenceof unseen map nodes - an important building block for qualitative active planning. We also conduct a study thatshows how well we can estimate unseen nodes. Our method particularly appeals to scenarios with few salientlandmarks and low-quality sensors. We evaluate our approach in simulation and on a real-world dataset and showits superior performance and low complexity compared to the state-of-the-art. Our analysis also indicates goodprospects for using qualitative navigation and planning in real-world scenarios.

The pharmacoinformation approach to the assessment and modeling of drugs involves the use of modern methods of data mining. These methods include: 1) analysis of big data (selection of texts of scientific publications, search for new biomarkers); 2) computer analysis of texts (automatic classification of texts by content, identification of pseudoscientific texts); 3) analysis of metric maps (visualization and analysis of complex patterns, including clustering) and 4) chemoinformation analysis, including the assessment of the effect of drugs on the transcriptome, proteome and microbiome of a person. The article provides examples of the application of these methods of pharmacoinformatics to chondroprotectors containing standardized forms of chondroitin sulfate and glucosamine sulfate.

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

This paper discusses the problem of a semantic topology map generation during the environment mapping process. A method is proposed which provides a model of a mobile robot’s working space. The proposed world representation is divided into two maps: semantic topology map and metric map. The first one is created by employing graph structure which describes relations between different zones generated during exploration of the environment, and the metric one which defines zones by expandable polygonal chain of border points. The acquired hierarchical world model can be used in the path planning and decision tasks in a human-friendly way by the means of semantic sentences. Such approach increases versatility of human-robot cooperation. The proposed algorithm has been validated in an experiment and the obtained results which shows a good performance of the devised method are presented.

In this paper, a new system for automatic drawing of bas-reliefs is presented. The system consists of a fringe projection scanner accompanied by a photometric stereo component. At first, the photometric stereo is used to accurately extract normals of the relief surface. Using a line tracing algorithm the normals are converted into 2D lines. Then, using the fringe projection scanner, the 2D lines are transformed to the 3D space to form a metric map required for cultural heritage applications. To evaluate the system, several tests were carried out using artificial and real bas-reliefs. The results showed that the system is able to draw 98.8% of the object lines correctly.

In the field of autonomous navigation for robotics, one of the most challenging issues is to locate the Next-Best-View and to guide robotics through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes a reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. To be specific, a fast and practical algorithm for constructing RAGVGs from metric maps is presented, and a RAGVG-based autonomous robotic exploration framework is designed and implemented. The proposed method for constructing RAGVGs is validated with two known common maps, while the RAGVG-based autonomous exploration framework is tested on two simulation and one real-world museum. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and indicate that the mobile robot controlled by the RAGVG-based autonomous exploration framework, compared with famous frontiers-based method, reduced the total time by approximately 20% for the given tasks.

We present a real-time monocular simultaneous localization and mapping (SLAM) system with a new distributed structure for multi-UAV collaboration tasks. The system is different from other general SLAM systems in two aspects: First, it does not aim to build a global map, but to estimate the latest relative position between nearby vehicles; Second, there is no centralized structure in the proposed system, and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles’. To realize the above characteristics in real time, we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs. Based on the hash and principal component analysis, the matching time complexity of this algorithm can be reduced from O(log N) to O(1). To evaluate the performance, the algorithm is verified on the acknowledged multi-view stereo benchmark dataset, and excellent results are obtained. Finally, through the simulation and real flight experiments, this improved SLAM system with the proposed algorithm is validated.

Autonomous robotic exploration has been extensively applied in many tasks, such as mobile mapping and indoor searching. One of the most challenging issues is to locate the Next-Best-View and to guide robots through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes an improving method based on reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. Additionally, a fast and robust image thinning algorithm for constructing RAGVGs from metric maps is presented, and an autonomous robotic exploration framework using RAGVGs is designed. The proposed method is validated with three known common data sets and two simulations of autonomous exploration tasks. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and the simulations indicate that the mobile robot controlled by the RAGVG-based exploration method reduced the total time by approximately 20% for the given tasks.

Purpose: A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolutional neural network (CNN) to overcome this problem. Methods: RSRI and magnetization transfer images from 20 healthy volunteers were included. A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the MVI map calculated from magnetization transfer images (MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by testing how well they correlated with the MTMVI map. The correlations were evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and (ii) pixel-based comparison for ROIs defined in four different tissue types (cortical and subcortical gray matter, white matter, and whole brain). Results: For atlas-based ROIs, the overall correlation with the MTMVI map was higher for the GenMVI map than for the SyMVF map. In the pixel-based comparison, correlation with the MTMVI map was stronger for the GenMVI map than for the SyMVF map, and the difference in the distribution for the volunteers was significant (Wilcoxon sign-rank test, P < 0.001) in all tissue types. Conclusion: The proposed method is useful, as it can incorporate more specific information about local tissue properties than the existing method. However, clinical validation is necessary.

Robots are expected to replace humans in performing scientific expedition tasks, which greatly reduces the essential impact of human basic survival on scientific expedition activities, and improve the space-time coverage of scientific expedition activities in polar regions. However, the polar regions have complex topographic features, such as the edge of the glacier on the Qinghai-Tibet Plateau is steep and the inland is relatively flat, which undoubtedly brings great challenges to realize the safe and autonomous navigation of unmanned ground vehicles. For glacier area of the Qinghai-Tibet Plateau, accurately converting environmental information into the map that robots can understand is an important part of path planning, that is, mapping. This paper proposes a method of constructing an occupancy grid map for large-scale point cloud data, taking into account the movement constraints and terrain information of the mobile platform. By dividing and rasterizing the sub-point cloud, the state of the raster can be judged according to the normal, elevation and other information. Finally, we carried out path planning based on the A* on the raster map.

In this paper, we proposed a localizability-based topological local object occupancy map (TLO2M) for homing navigation. The proposed approach is a combination of topological and metric map representations. We utilize object detection to advance the occupancy grid map and train a structural localizability measuring network with it. As a result, the TLO2M is built based on structural localizability and feature similarity. The proposed method shows a 0.955 success rate of the homing task at the Gibson environments.

We describe a deep-geometric localizer that is able to estimate the full six degrees-of-freedom (DOF) global pose of the camera from a single image in a previously mapped environment. Our map is a topo-metric one, with discrete topological nodes whose 6DOF poses are known. Each topo-node in our map also comprises of a set of points, whose 2D features and 3D locations are stored as part of the mapping process. For the mapping phase, we utilize a stereo camera and a regular stereo visual SLAM pipeline. During the localization phase, we take a single camera image, localize it to a topological node using deep learning, and use a geometric algorithm (perspective-n-point (PnP)) on the matched 2D features (and their 3D positions in the topo map) to determine the full 6DOF globally consistent pose of the camera. Our method divorces the mapping and the localization algorithms and sensors (stereo and mono) and allows accurate 6DOF pose estimation in a previously mapped environment using a single camera. With results in simulated and real environments, our hybrid algorithm is particularly useful for autonomous vehicles (AVs) and shuttles that might repeatedly traverse the same route.

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.

Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than the previous state-of-the-art methods.

BACKGROUND: Segmentation is a crucial and necessary step in diffusion tensor imaging (DTI) analysis of the cervical spinal cord. In existing studies, different diffusive metric maps [B0, fractional anisotropy (FA) and axial diffusivity (AD) maps] have been involved in the segmentation of tissues of the cervical spinal cord. The selection of a diffusive metric map for segmentation may affect the accuracy of segmentation and then affect the validity and effectiveness of the extracted diffusive features. However, there are few discussions on this problem. Therefore, this study would like to examine the effect of segmentation based on different diffusive metric maps for DTI analysis of the cervical spinal cord. METHODS: Twenty-nine healthy subjects and thirty patients with cervical spondylotic myelopathy (CSM) were finally included in this study. All subjects accepted DTI scanning at cervical levels from C2 to C7/T1. For healthy subjects, all cervical levels were included for analysis; while, for each patient, only one compressed cervical level was included. After DTI scanning, DTI metrics including B0, FA, AD, radial diffusivity (RD) and mean diffusivity (MD) were calculated. The evaluation was performed to B0, FA and AD maps from two aspects. First, the accuracy of segmentation was evaluated via a comparison between segmentation based on each diffusive metric map and segmentation based on an average image, which was acquired by averaging B0, FA, AD, RD and MD maps. The segmentation was achieved by a semi-automatic segmentation process, and the similarity between two segmentation results was denoted by the intersection of the union (IOU). Second, the diversity of extracted diffusive features was equalized as their performance in the classification of image pixels of different regions of interest (ROIs) and then was evaluated by mutual information (MI) and area under the curve (AUC). One-way ANOVA and Bonferroni’s post hoc tests were applied to compare the evaluation results. RESULTS: One-way ANOVA suggested that there were differences (P

Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS) requires accurate vehicle, environment maps, and risk models to assure flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight. We define map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graph-based and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.

A new class of metric measure spaces is introduced and studied. This class generalises the well-established doubling metric measure spaces as well as the spaces $(\mathbb{R}^n,\mu)$ with $\mu(B(x,r))\leq Cr^d$, in which non-doubling harmonic analysis has recently been developed. It seems to be a promising framework for an abstract extension of this theory. Tolsa's space of regularised BMO functions is defined in this new setting, and the John-Nirenberg inequality is proven.

A sketch map represents an individual&rsquo, s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads.

In this paper, we propose a mapping technique that builds a continuous representation of the environment from range data. The strategy presented here encodes the probability of points in space to be occupied using 2.5D B-spline surfaces. For a fast update rate, the surface is recursively updated as new measurements arrive. The proposed B-spline map is less susceptible to precision and interpolation errors that are present in occupancy grid-based methods. From simulation and experimental results, we show that this approach leverages the floating point resolution of continuous metric maps and the fast update/access/merging advantages of discrete metric maps. Thus, the proposed method is suitable for online robotic tasks such as localization and path planning, requiring minor modification to existing software that usually operates on metric maps.

Localization technology is essential for robots. The map created to recognize the location mainly contains metric information. However, in a changing environment, a Semantic Map containing Semantic object information is required a multi-modal sensor composed of multiple types and multiple sensors[RGBD, thermal, night vision, global shutter camera, microphone, 16 channel laser sensor(=Lidar)] was created for semantic information recognition and semantic map creation in various environments, and calibration was performed to integrate the coordinate system. After that, we introduce the method of generating the metric map according to the configuration of the multi-modal sensor. Also, we propose a method to obtain a single accurate location by integrating the location recognition results obtained from various maps. This can be used to specify the position of the semantic object. Finally, it can be expected that the semantic object and the semantic map information obtained through the multi-modal sensor can be used for various different sensor configurations and various types of robots.

Meaningful 3D maps have a lot to offer to the design of safe and intelligent transportation systems. To do so, street content such as cars, traffic lights and signs need to be segmented in their 3D form, and accurately localized in a map. This paper proposes a first step towards producing 3D rich urban maps in which cars are segmented from a point cloud, and rendered in their true form on a metric map. Our system is based on the integration of stereo SLAM for point cloud extraction, 3D car detection, shape completion, meshing, and optimization of camera pose based on the detected cars. We test our system on the KITTI dataset and produce very realistic maps. As a second contribution, we assess the effect of including the detected cars as objects in a semantic Simultaneous Localization and Mapping (SLAM) pipeline, and demonstrate the potential for improved localization.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies Sketch maps play an important role in communicating spatial knowledge, particularly in applications interested in identifying correspondences to metric maps for land tenure in rural communities. The interpretation of a sketch map is linked to the users’ spatial reasoning and the number of features included. Additionally, in order to make use of the information provided by sketch maps, the integration with information systems is needed but is convoluted. The process of identifying which element in the base map is being represented in the sketch map involves the use of correct descriptors and structures to manage them. In the past years, different methods to give a solution to the sketch matching problem employs iterative methods using static scores to create a subset of correspondences. In this thesis, we propose an implementation for the automatic aligning of the sketch to metric maps, based on dynamic programming techniques from reinforcement learning. Our solution is distinctive from other approaches as it searches for pair equivalences by exploring the environment of the search space and learning from positive rewards derived from a custom scoring system. Scores are used to evaluate the likeliness of a candidate pair to belong to the final solution, and the results are back up in a state-value function to recover the best subset states and recovering the highest scored combinations. Reinforcement learning algorithms are dynamic and robust solutions for finding the best solution in an ample search space. The proposed workflow improves the outcoming spatial configuration for the aligned features compared to previous approaches, specifically the Tabu Search.

Advanced systems for manned and/or agricultural vehicles&mdash, such as systems for auto-steering, navigation-adding, and autonomous route planning&mdash, require new capabilities in terms of the internal representation for the autonomous system of the working space, that is, the generation of a metric map that provides by numerical parameters any operation-related entity of the working space. In this paper, a real-time approach was developed for the generation of the field metric map, based on a row generation method (polygons-based geometry). The approach can deal with fields with or without in-field obstacles, where the generated field-work tracks can be either straight or curved. The functionality of the approach was demonstrated on 12 fields with different number of obstacles ranging from one to six. The test results showed that the computational times were in the range of 0.26&ndash, 24.51 s. The presented tool brings a number of advancements on the process of generating a metric map for arable farming field operations, including the real-time generation feature, the potential to deal with multiple-obstacle areas, and the reduction in the overlapped area.

State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map., Comment: for test implementation check: https://github.com/tkucner/rose

In order to achieve an intuitive interaction and visual semantic navigation for the indoor robot, we propose a novel object-aware hybrid map. The existing map is usually a metric map, lacking semantics for interaction. We combine objects in the indoor environment with the metric map to constitute a hybrid map. The map consists of a 3D object semantic map and a 2D occupancy grid map, which transfers human commands to the grid map through object semantics, thereby enabling autonomous navigation for the robot. We utilize ORB-SLAM2 for continuous pose estimation and 3D mapping. 2D object detection in key-frames is conducted based on YOLO v3. The object point clouds in multiple perspectives are merged and a 3D bounding box of the object is estimated. These objects construct a 3D semantic map. Furthermore, we project a 3D point cloud map into a 2D plane in order to get an occupancy grid map. Finally, these two maps are combined forming an object-aware hybrid map. We conduct experiments in real environments in order to verify the feasibility and robustness of the hybrid map for robot semantic navigation.

Autonomous navigation is a basic prerequisite for mobile robot to realize environmental exploration. Current navigation methods are mainly based on metric maps, which require precise geometric coordinates and lack the capability to efficiently store semantic information of the environment. In this paper, we present a visual topological mapping and navigation method for mobile robot in large-scale environment, which is similar to the human navigation system. Topological map represents the environment as a topology diagram with nodes and edges in which the topological nodes record local semantic information of the environment, such as visual features, robot pose and scene properties. In the topological navigation stage, an image-based Monte Carlo localization is proposed to estimate the semantic pose of robot which can help robot judge whether it has reached the target location more flexibility. Experiments are conducted in real world environments and results indicate that the proposed system exhibits great performance in robustness of navigation.

This work is intended to study the stages of exploring, localization and mapping of autonomous mobile robots and vehicles. In addition to the use of integrated and standard software, ROS has the possibility of creating small map data files recorded with the data provided by 2D Light Detection And Ranging (LiDAR) sensors. The low data density favours the increased efficiency during data processing. The metric maps register just enough information to create the topological nodes and edges in a relational map. Extensive experiments in both simulated environments and real-world applications show the effectiveness of the proposed method.

Many digital images contain blurred regions which are caused by motion or defocus. The defocus blur reduces the contrast and sharpness detail of the image. Automatic blur detection and segmentation is an important and challenging task in the field of Computer vision “e.g. object recognition and scene interpretation” that requires the extraction and processing of large amounts of data from sharp areas of the image. Therefore, the sharp and blur areas must be segmented separately to assure that the information is extracted from the sharp regions. The existing techniques on blur detection and segmentation have taken a lot of effort and time to design metric maps of local clarity. Furthermore, these methods have various limitations “i.e. low accuracy rate in noisy images, detecting blurred smooth and sharp smooth regions, and high execution cost”. Therefore, there is a dire necessity to propose a method for the detection and segmentation of defocus blur robust to the aforementioned limitations. In this paper, we present a novel defocus blur detection and segmentation algorithm, “Local Triplicate Co-occurrence Patterns” (LTCoP) for the separation of in-focus and out-of-focus regions. It is observed that the fusion of extracted higher and lower patterns of LTCoP produces far better results than the others. To test the effectiveness of our algorithm, the proposed method is compared with several state-of-the-art techniques over a large number of sample images. The experimental results show that the proposed technique obtains comparative results with state-of-the-art methods and offers a significant high-speed advantage over them. Therefore, we argue that the proposed method can reliably be used for defocus blur detection and segmentation in high-density images.

This paper develops a theory of harmonic analysis on spaces with tree metrics, extending previous work in this direction by Gavish, Nadler and Coifman (2010) [30] and Gavish and Coifman (2011, 2012) [28] , [29] . We show how a natural system of martingales and martingale differences induced by a partition tree leads to simple and effective characterizations of the Lipschitz norm and its dual for functions on a single tree metric space. The restrictions we place on the tree metrics are far more general than those considered in previous work. As the dual norm is equal to the Earth Mover's Distance (EMD) between two probability distributions, we recover a simple formula for EMD with respect to tree distances presented by Charikar (2002) [36] . We also consider the situation where an arbitrary metric is approximated by the average of a family of dominating tree metrics. We show that the Lipschitz norm and its dual for the tree metrics can be combined to yield an approximation to the corresponding norms for the underlying metric. The main contributions of this paper, however, are the generalizations of the aforementioned results to the setting of the product of two or more tree metric spaces. For functions on a product space, the notion of regularity we consider is not the Lipschitz condition, but rather the mixed Lipschitz condition that controls the size of a function's mixed difference quotient. This condition is extremely natural for datasets that can be described as a product of metric spaces, such as word-document databases. We develop effective formulas for norms equivalent to the mixed Lipschitz norm and its dual, and extend our results on combining pairs of trees.

This paper presents a novel multi-sensor navigation system for the urban ground vehicle. The integration system can combine the measurements from a polarized skylight sensor, an inertial sensor, and a monocular camera. Utilizing the polarized skylight sensor, we propose a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system. In our algorithm, the ambiguity problem of polarized orientation is solved without any other sensor. In order to enhance the algorithm's robustness in the urban environment, we also propose a real-time method that uses the gradient of the degree of the polarization to remove the obstacles. With a monocular camera, we build a metric map and recognize places in the map to provide the position constraint for the integrating system. We develop the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle. The results demonstrate that our proposed system outperforms other vision-based navigation algorithms-the RMSE of the position error is 2.04 m (0.01% of the travelled distance) and the RMSE of the orientation error is 0.84°. Finally, we present interesting insights gained with respect to the further work in sensors and robotics.

[EN] In this paper we develop a new technique for constructing fuzzy metric spaces, in the sense of George and Veeramani, from metric spaces and by means of the Lukasievicz t-norm. In particular such a technique is based on the use of metric preserving functions in the sense of J. Dobos. Besides, the new generated fuzzy metric spaces are strong and completable, and if we add an extra condition, they are principal. Appropriate examples of such fuzzy metric spaces are given in order to illustrate the exposed technique., Valentín Gregori acknowledges the support of the Spanish Ministry of Economy and Competitiveness under Grant MTM2015-64373-P (MINECO/FEDER, UE). Oscar Valero acknowledges the support of the Spanish Ministry of Economy and Competitiveness under Grants TIN2014-56381-REDT (LODISCO), TIN2016-81731-REDT (LODISCO II) and AEI/FEDER, UE funds.

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken., Published in the Special Issue "Frontiers in Mobile Robot Navigation" of Sensors. https://www.mdpi.com/1424-8220/21/22/7562

In this paper, we establish a new best proximity point theorem for Roger–Hardy type generalized F-contraction mappings and nonexpansive mappings in complete metric spaces. Also, we give some examples for supporting main results. Moreover, our results improve and extend several results in literature.

We prove that the metric projection onto a finite-dimensional subspace Y ⊂ L p, p ∈ (1, 2) ∪ (2, ∞), satisfies the Lipschitz condition if and only if every function in Y is supported on finitely many atoms. We estimate the Lipschitz constant of such a projection for the case in which the subspace is one-dimensional.