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ros occupancy grid github
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ros occupancy grid github
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ros occupancy grid github
ros occupancy grid github
This data set has been made publicly available for other researchers to use. Williams et al. The dataset consists of five subdatasets. Pei et al. How do I fix the You called InitGoogleLogging() twice! error. Now the resource intensive packages and the ones depending on ROS1 packages: Go grab a coffee (or a meal if you compile on ARM). However, processing a covariance matrix is a significant challenge as it grows with the number of landmarks. Then get the turtlebo2 demos specific code: For resource constrained platforms we will split the build into 2 steps to make sure not to overflow the memory. The application uses ROS nodes initializing publisher and subscriber with standard ROS messages. Their method restricts the free movement of the slave agent. This monocular visual SLAM method worked in a room-sized environment. Agents use these coincidental encounters to find their relative locations. Given that the relative transformation between nodes is known for connected exploring nodes, these cubes are placed correctly in the map. Here and are relative pose transformations and corresponding covariance matrix between th and th the key frames, respectively. Work fast with our official CLI. The following settings and options are exposed to you. The following instructions are for ROS Bouncy, if you are using ROS Ardent please refer to these instructions. The absolute pose is encoded with a translation, along with orientation and scale parameters using a quaternion. ROS-Lite: ROS Framework for NoC-Based Embedded Many-Core Platform: 0876: Utilizing Sacrificial Molding for Embedding Motion Controlling Endostructures in Soft Pneumatic Actuators: 0879: Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments: 0888: SSP: Single If nothing happens, download GitHub Desktop and try again. Furthermore, the smaller form factor and the lower cost of cameras also contribute to this choice. Enter the Value for your secret. Camera (sensor_msgs/Image, sensor_msgs/CompressedImage). The entire system is initialized by positioning the camera in front of a marker. The planning results are visualized in Rviz as following: The planner can also take input polygonal map for collision checking. Usage. Next, agents performed SLAM and estimated their new locations, while at the same time communicating their locations to each other. Each exploring node maintains a set of key frames and a pose graph to represent the map. 7, pp. Upon receiving a loop closure command with , the exploring node checks whether and are consecutive key frames in the pose graph. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Their analysis hence favors bundle adjustment techniques over incremental methods for accurate monocular visual SLAM. In S01-A-P20, we rotated the camera around its -axis by 20. Copyright (c) 2020 Nils Rottmann, Nico Studt. Mobile Android Device with Android Version 5.0 (Lollipop) or higher, Android Studio Version 3.6.1 or higher (if built from source), Install Android Studio Version 3.6.1 or higher, Download the complete repository (Master Branch) and open it via Android Studio. Once the connection is made, each exploring node sends its map to its counterpart. ROS-Mobile. in [23] used a centralized ground station for mapping, loop closure detection, and map merging. Please We show the matched features between key frames, The fusion graph showing exploring nodes (, The map merging process of the fusion graph edge. 1. Each exploring node performs a semidense visual SLAM by using a camera as the only sensor, based on the work by [16]. Assume that the fusion graph edge having the largest satisfies where is an empirical threshold. [13] introduced a pose graph optimization technique that corrects the scale drift at loop closures. Dieter et al. Their method handled large looped trajectories well. Are you using ROS 2 (Dashing/Foxy/Rolling)? Reconfiguring Metamorphic Robots Via SMT: Is It a Viable Way? 167193, Springer-Verlag, New York, NY, USA, 1990. R. Smith, M. Self, and P. Cheeseman, Estimating uncertain spatial relationships in robotics, in Autonomous Robot Vehicles, I. J. Cox and G. T. Wilfong, Eds., pp. If there is a map of the environment, the agents can utilize it to localize themselves in it. They argue that the former increases the accuracy of the motion estimation and a better map estimation for a given computational budget. The SLAM process simultaneously tracks the camera against the current key frame and improves its and based on its new observations. TUK Campus Dataset, Stereo Waterdrop Removal with Row-Wise Dilated Attention, Temporally-Continuous Probabilistic Prediction Using Polynomial Trajectory Parameterization, Content Disentanglement for Semantically Consistent Synthetic-To-Real Domain Adaptation, Cross-Modal 3D Object Detection and Tracking for Auto-Driving, Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation, Real-Time Physically-Accurate Simulation of Robotic Snap Connection Process, Fundamental Challenges in Deep Learning for Stiff Contact Dynamics, Multi-Contact Locomotion Planning with Bilateral Contact Forces Considering Kinematics and Statics During Contact Transition, Computationally Efficient HQP-Based Whole-Body Control Exploiting the Operational-Space Formulation, Towards an Online Framework for Changing-Contact Robot Manipulation Tasks, Experimental Verification of Stability Theory for a Planar Rigid Body with Two Unilateral Frictional Contacts (I), Sensor Fusion-Based Anthropomorphic Control of Under-Actuated Bionic Hand in Dynamic Environment, Model-Based Trajectory Prediction and Hitting Velocity Control for a New Table Tennis Robot, Active Exploration and Mapping Via Iterative Covariance Regulation Over Continuous SE(3) Trajectories, Modeling and Control of PANTHERA Self-Reconfigurable Pavement Sweeping Robot under Actuator Constraints, Coloured Petri Nets for Monitoring Human Actions in Flexible Human-Robot Teams, Adaptive Passivity-Based Multi-Task Tracking Control for Robotic Manipulators, Amplification of Clamping Mechanism Using Internally-Balanced Magnetic Unit, Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots with Terra-Mechanical Constraints, Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games, The Radar Ghost Dataset an Evaluation of Ghost Objects in Automotive Radar Data, ChangeSim: Towards End-To-End Online Scene Change Detection in Industrial Indoor Environments, Indoor Future Person Localization from an Egocentric Wearable Camera, Grounding Linguistic Commands to Navigable Regions, TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset, Diverse Complexity Measures for Dataset Curation in Self-Driving, A Dataset for Provident Vehicle Detection at Night, Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments, NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences, Topo-Boundary: A Benchmark Dataset on Topological Road-Boundary Detection Using Aerial Images for Autonomous Driving, ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking, A Large-Scale Dataset for Water Segmentation of SAR Satellite, ESPADA: Extended Synthetic and Photogrammetric Aerial-Image Dataset, Adversarial Training on Point Clouds for Sim-To-Real 3D Object Detection, CrossMap Transformer: A Crossmodal Masked Path TransformerUsing Double Back-Translation for Vision-And-Language Navigation, Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions, Target-Dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots, Self-Critical Learning of Influencing Factors for Trajectory Prediction Using Gated Graph Convolutional Network, Trajectory Generation in New Environments from past Experiences, DistillPose: Lightweight Camera Localization Using Auxiliary Learning, Identifying Valid Robot Configurations Via a Deep Learning Approach, DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks, StyleLess Layer: Improving Robustness for Real-World Driving, Annotation Cost Reduction of Stream-Based Active Learning by Automated Weak Labeling Using a Robot Arm, Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan, A CNN Based Vision-Proprioception Fusion Method for Robust UGV Terrain Classification, Visual-Tactile Cross-Modal Data Generation Using Residue-Fusion GAN with Feature-Matching and Perceptual Losses, Geometry Guided Network for Point Cloud Registration, Graph Guided Deformation for Point Cloud Completion, Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks, ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation, Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization, MLPD: Multi-Label Pedestrian Detector in Multispectral Domain, Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras, EVReflex: Dense Time-To-Impact Prediction for Event-Based Obstacle Avoidance, PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds, A Registration-Aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition, INeRF: Inverting Neural Radiance Fields for Pose Estimation, RaP-Net: A Region-Wise and Point-Wise Weighting Network to Extract Robust Features for Indoor Localization, Differentiable Factor Graph Optimization for Learning Smoothers, Attention Augmented ConvLSTM for Environment Prediction, Overcoming Obstructions Via Bandwidth-Limited Multi-Agent Spatial Handshaking, Scene Descriptor Expressing Ambiguity in Information Recovery Based on Incomplete Partial Observation, Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation, Automatic Learning System for Object Function Points from Random Shape Generation and Physical Validation, Fast Image-Anomaly Mitigation for Autonomous Mobile Robots, Visual Identification of Articulated Object Parts, Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints, ViNet: Pushing the Limits of Visual Modality for Audio-Visual Saliency Prediction, MDN-VO: Estimating Visual Odometry with Confidence, Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments, Correlate-And-Excite: Real-Time Stereo Matching Via Guided Cost Volume Excitation, Improving Robot Localisation by Ignoring Visual Distraction, Semantic Segmentation-Assisted Scene Completion for LiDAR Point Clouds, Dynamic Domain Adaptation for Single-View 3D Reconstruction, You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module, VIPose: Real-Time Visual-Inertial 6D Object Pose Tracking, Using Visual Anomaly Detection for Task Execution Monitoring, Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes, Pose Estimation from RGB Images of Highly Symmetric Objects Using a Novel Multi-Pose Loss and Differential Rendering, Denoising 3D Human Poses from Low-Resolution Video Using Variational Autoencoder, KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation, All Characteristics Preservation: Single Image Dehazing Based on Hierarchical Detail Reconstruction Wavelet Decomposition Network, PCTMA-Net: Point Cloud Transformer with Morphing Atlas-Based Point Generation Network for Dense Point Cloud Completion, Superline: A Robust Line Segment Feature for Visual SLAM, ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images, Model Adaptation through Hypothesis Transfer with Gradual Knowledge Distillation, VoluMon: Weakly Supervised Volumetric Monocular Estimation with Ellipsoid Representations, Cross-Modal Representation Learning for Lightweight and Accurate Facial Action Unit Detection, Stereo Matching by Self-Supervision of Multiscopic Vision, Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation, Efficient Learning of Goal-Oriented Push-Grasping Synergy in Clutter, Iterative Coarse-To-Fine 6D-Pose Estimation Using Back-Propagation, Understanding Human Manipulation with the Environment: A Novel Taxonomy for Video Labelling, Excavation Learning for Rigid Objects in Clutter, Fast-Learning Grasping and Pre-Grasping Via Clutter Quantization and Q-Map Masking, Joint Space Control Via Deep Reinforcement Learning, Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers, Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter, Double-Dot Network for Antipodal Grasp Detection, Neural Motion Prediction for In-Flight Uneven Object Catching, Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation, Occlusion-Aware Search for Object Retrieval in Clutter, Grasp Pose Detection from a Single RGB Image, DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping, Reactive Long Horizon Task Execution Via Visual Skill and Precondition Models, Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3), DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration, Graph-Based Task-Specific Prediction Models for Interactions between Deformable and Rigid Objects, GhostPose*: Multi-View Pose Estimation of Transparent Objects for Robot Hand Grasping, Reinforcement Learning for Vision-Based Object Manipulation with Non-Parametric Policy and Action Primitives, Casting Manipulation of Unknown String by Robot Arm, Deformation Control of a Deformable Object Based on Visual and Tactile Feedback, A Soft Robotic Gripper with an Active Palm and Reconfigurable Fingers for Fully Dexterous In-Hand Manipulation, The Stewart Hand: A Highly Dexterous 6-Degrees-Of-Freedom Manipulator Based on the Stewart-Gough Platform (I), Real-Time Safety and Control of Robotic Manipulators with Torque Saturation in Operational Space, Robot Hand Based on a Spherical Parallel Mechanism for Within-Hand Rotations about a Fixed Point, Learning Compliant Grasping and Manipulation by Teleoperation with Adaptive Force Control, Optimal Scheduling and Non-Cooperative Distributed Model Predictive Control for Multiple Robotic Manipulators, OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation, Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots, Learning Connectivity for Data Distribution in Robot Teams, Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments, Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells, Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking, State Estimation and Model-Predictive Control for Multi-Robot Handling and Tracking of AGV Motions Using IGPS, Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks, Assembly Sequence Generation for New Objects Via Experience Learned from Similar Object, Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks, Learn to Differ: Sim2Real Small Defection Segmentation Network, Control Strategy for Jam and Wedge-Free 3D Precision Insertion of Heavy Objects Suspended with a Multi-Cable Crane, Combining Unsupervised Muscle Co-Contraction Estimation with Bio-Feedback Allows Augmented Kinesthetic Teaching, 3D Reactive Control and Frontier-Based Exploration for Unstructured Environments, Adaptive Terrain Traversability Prediction Based on Multi-Source Transfer Gaussian Processes, Multiclass Terrain Classification Using Sound and Vibration from Mobile Robot Terrain Interaction, Perceptive Autonomous Stair Climbing for Quadrupedal Robots, Trajectory Selection for Power-Over-Tether Atmospheric Sensing UAS, CCRobot-IV: An Obstacle-Free Split-Type Quad-Ducted Propeller-Driven Bridge Stay Cable-Climbing Robot, An Industrial Robot for Firewater Piping Inspection and Mapping, A Mixed Reality Supervision and Telepresence Interface for Outdoor Field Robotics, A Soft Somesthetic Robotic Finger Based on Conductive Working Liquid and an Origami Structure, Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects, AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection, A Low-Cost Modular System of Customizable, Versatile, and Flexible Tactile Sensor Arrays, Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration, A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration, A Multi-Axis FBG-Based Tactile Sensor for Gripping in Space, Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace, High Dynamic Range 6-Axis Force Sensor Employing a Semiconductor-Metallic Foil Strain Gauge Combination, Tactile Scanning for Detecting Micro Bump by Strain-Sensitive Artificial Skin, A Force Recognition System for Distinguishing Click Responses of Various Objects, A Robust Controller for Stable 3D Pinching Using Tactile Sensing, Dynamic Modeling of Hand-Object Interactions Via Tactile Sensing, A Local Filtering Technique for Robot Skin Data, Energy Generating Electronic Skin with Intrinsic Tactile Sensing without Touch Sensors (I), Sensor Selection for Detecting Deviations from a Planned Itinerary, Autonomous Decision-Making with Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning, Automata-Based Optimal Planning with Relaxed Specifications, Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learning, Learning from Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains, Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots, A Topological Approach to Finding Coarsely Diverse Paths, Probabilistic Specification Learning for Planning with Safety Constraints, Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic, Safe Linear Temporal Logic Motion Planning in Dynamic Environments, Decentralized Classification with Assume-Guarantee Planning, Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference, Formalizing the Execution Context of Behavior Trees for Runtime Verification of Deliberative Policies, Probabilistic Trajectory Prediction with Structural Constraints, Formalizing Trajectories in Human-Robot Encounters Via Probabilistic STL Inference, Convex Approximation for LTL-Based Planning, Temporal Force Synergies in Human Grasping, Trajectory-Based Split Hindsight Reverse Curriculum Learning, Detecting Grasp Phases and Adaption of Object-Hand Interaction Forces of a Soft Humanoid Hand Based on Tactile Feedback, SpectGRASP: Robotic Grasping by Spectral Correlation, Assessing Grasp Quality Using Local Sensitivity Analysis, Geometry-Based Grasping Pipeline for Bi-Modal Pick and Place, Computing a Task-Dependent Grasp Metric Using Second-Order Cone Programs, Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp, PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning, Geometric Characterization of the Planar Multi-Finger Equilibrium Grasps, Formulation and Validation of an Intuitive Quality Measure for Antipodal Grasp Pose Evaluation, Scooping Manipulation Via Motion Control with a Two-Fingered Gripper and Its Application to Bin Picking, DDGC: Generative Deep Dexterous Grasping in Clutter, Planning Grasps with Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes (I), A Three-Fingered Adaptive Gripper with Multiple Grasping Modes, Dexterous Textile Manipulation Using Electroadhesive Fingers, A Series Elastic, Compact Differential Mechanism: On the Development of Adaptive, Lightweight Robotic Grippers and Hands, Computational Design of Reconfigurable Underactuated Linkages for Adaptive Grippers, A Multi-Modal Robotic Gripper with a Reconfigurable Base: Improving Dexterous Manipulation without Compromising Grasping Efficiency, Grasping with Embedded Synergies through a Reconfigurable Electric Actuation Topology, An Under-Actuated Whippletree Mechanism Gripper Based on Multi-Objective Design Optimization with Auto-Tuned Weights, A Caging Inspired Gripper Using Flexible Fingers and a Movable Palm, The Role of Digit Arrangement in Soft Robotic In-Hand Manipulation, A Dexterous, Reconfigurable Robot Hand Combining Anthropomorphic and Interdigitated Configurations, A Computational Framework for Robot Hand Design Via Reinforcement Learning, Variable-Grasping-Mode Gripper with Different Finger Structures for Grasping Small-Sized Items, Force Control with Friction Compensation in a Pneumatic Gripper, Analysis of Fingertip Force Vector for Pinch-Lifting Gripper with Robust Adaptation to Environments (I), Design and Validation of a Smartphone-Based Haptic Feedback System for Gait Training, Robotic Guidance System for Visually Impaired Users Running Outdoors Using Haptic Feedback, Variable Stiffness Folding Joints for Haptic Feedback. hhJOyh, fHSZ, TYS, wsjwnT, cTosi, Eyr, FgGHZM, kTq, gWeoU, uDjs, 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This data set has been made publicly available for other researchers to use. Williams et al. The dataset consists of five subdatasets. Pei et al. How do I fix the You called InitGoogleLogging() twice! error. Now the resource intensive packages and the ones depending on ROS1 packages: Go grab a coffee (or a meal if you compile on ARM). However, processing a covariance matrix is a significant challenge as it grows with the number of landmarks. Then get the turtlebo2 demos specific code: For resource constrained platforms we will split the build into 2 steps to make sure not to overflow the memory. The application uses ROS nodes initializing publisher and subscriber with standard ROS messages. Their method restricts the free movement of the slave agent. This monocular visual SLAM method worked in a room-sized environment. Agents use these coincidental encounters to find their relative locations. Given that the relative transformation between nodes is known for connected exploring nodes, these cubes are placed correctly in the map. Here and are relative pose transformations and corresponding covariance matrix between th and th the key frames, respectively. Work fast with our official CLI. The following settings and options are exposed to you. The following instructions are for ROS Bouncy, if you are using ROS Ardent please refer to these instructions. The absolute pose is encoded with a translation, along with orientation and scale parameters using a quaternion. ROS-Lite: ROS Framework for NoC-Based Embedded Many-Core Platform: 0876: Utilizing Sacrificial Molding for Embedding Motion Controlling Endostructures in Soft Pneumatic Actuators: 0879: Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments: 0888: SSP: Single If nothing happens, download GitHub Desktop and try again. Furthermore, the smaller form factor and the lower cost of cameras also contribute to this choice. Enter the Value for your secret. Camera (sensor_msgs/Image, sensor_msgs/CompressedImage). The entire system is initialized by positioning the camera in front of a marker. The planning results are visualized in Rviz as following: The planner can also take input polygonal map for collision checking. Usage. Next, agents performed SLAM and estimated their new locations, while at the same time communicating their locations to each other. Each exploring node maintains a set of key frames and a pose graph to represent the map. 7, pp. Upon receiving a loop closure command with , the exploring node checks whether and are consecutive key frames in the pose graph. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Their analysis hence favors bundle adjustment techniques over incremental methods for accurate monocular visual SLAM. In S01-A-P20, we rotated the camera around its -axis by 20. Copyright (c) 2020 Nils Rottmann, Nico Studt. Mobile Android Device with Android Version 5.0 (Lollipop) or higher, Android Studio Version 3.6.1 or higher (if built from source), Install Android Studio Version 3.6.1 or higher, Download the complete repository (Master Branch) and open it via Android Studio. Once the connection is made, each exploring node sends its map to its counterpart. ROS-Mobile. in [23] used a centralized ground station for mapping, loop closure detection, and map merging. Please We show the matched features between key frames, The fusion graph showing exploring nodes (, The map merging process of the fusion graph edge. 1. Each exploring node performs a semidense visual SLAM by using a camera as the only sensor, based on the work by [16]. Assume that the fusion graph edge having the largest satisfies where is an empirical threshold. [13] introduced a pose graph optimization technique that corrects the scale drift at loop closures. Dieter et al. Their method handled large looped trajectories well. Are you using ROS 2 (Dashing/Foxy/Rolling)? Reconfiguring Metamorphic Robots Via SMT: Is It a Viable Way? 167193, Springer-Verlag, New York, NY, USA, 1990. R. Smith, M. Self, and P. Cheeseman, Estimating uncertain spatial relationships in robotics, in Autonomous Robot Vehicles, I. J. Cox and G. T. Wilfong, Eds., pp. If there is a map of the environment, the agents can utilize it to localize themselves in it. They argue that the former increases the accuracy of the motion estimation and a better map estimation for a given computational budget. The SLAM process simultaneously tracks the camera against the current key frame and improves its and based on its new observations. TUK Campus Dataset, Stereo Waterdrop Removal with Row-Wise Dilated Attention, Temporally-Continuous Probabilistic Prediction Using Polynomial Trajectory Parameterization, Content Disentanglement for Semantically Consistent Synthetic-To-Real Domain Adaptation, Cross-Modal 3D Object Detection and Tracking for Auto-Driving, Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation, Real-Time Physically-Accurate Simulation of Robotic Snap Connection Process, Fundamental Challenges in Deep Learning for Stiff Contact Dynamics, Multi-Contact Locomotion Planning with Bilateral Contact Forces Considering Kinematics and Statics During Contact Transition, Computationally Efficient HQP-Based Whole-Body Control Exploiting the Operational-Space Formulation, Towards an Online Framework for Changing-Contact Robot Manipulation Tasks, Experimental Verification of Stability Theory for a Planar Rigid Body with Two Unilateral Frictional Contacts (I), Sensor Fusion-Based Anthropomorphic Control of Under-Actuated Bionic Hand in Dynamic Environment, Model-Based Trajectory Prediction and Hitting Velocity Control for a New Table Tennis Robot, Active Exploration and Mapping Via Iterative Covariance Regulation Over Continuous SE(3) Trajectories, Modeling and Control of PANTHERA Self-Reconfigurable Pavement Sweeping Robot under Actuator Constraints, Coloured Petri Nets for Monitoring Human Actions in Flexible Human-Robot Teams, Adaptive Passivity-Based Multi-Task Tracking Control for Robotic Manipulators, Amplification of Clamping Mechanism Using Internally-Balanced Magnetic Unit, Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots with Terra-Mechanical Constraints, Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games, The Radar Ghost Dataset an Evaluation of Ghost Objects in Automotive Radar Data, ChangeSim: Towards End-To-End Online Scene Change Detection in Industrial Indoor Environments, Indoor Future Person Localization from an Egocentric Wearable Camera, Grounding Linguistic Commands to Navigable Regions, TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset, Diverse Complexity Measures for Dataset Curation in Self-Driving, A Dataset for Provident Vehicle Detection at Night, Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments, NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences, Topo-Boundary: A Benchmark Dataset on Topological Road-Boundary Detection Using Aerial Images for Autonomous Driving, ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking, A Large-Scale Dataset for Water Segmentation of SAR Satellite, ESPADA: Extended Synthetic and Photogrammetric Aerial-Image Dataset, Adversarial Training on Point Clouds for Sim-To-Real 3D Object Detection, CrossMap Transformer: A Crossmodal Masked Path TransformerUsing Double Back-Translation for Vision-And-Language Navigation, Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions, Target-Dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots, Self-Critical Learning of Influencing Factors for Trajectory Prediction Using Gated Graph Convolutional Network, Trajectory Generation in New Environments from past Experiences, DistillPose: Lightweight Camera Localization Using Auxiliary Learning, Identifying Valid Robot Configurations Via a Deep Learning Approach, DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks, StyleLess Layer: Improving Robustness for Real-World Driving, Annotation Cost Reduction of Stream-Based Active Learning by Automated Weak Labeling Using a Robot Arm, Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan, A CNN Based Vision-Proprioception Fusion Method for Robust UGV Terrain Classification, Visual-Tactile Cross-Modal Data Generation Using Residue-Fusion GAN with Feature-Matching and Perceptual Losses, Geometry Guided Network for Point Cloud Registration, Graph Guided Deformation for Point Cloud Completion, Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks, ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation, Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization, MLPD: Multi-Label Pedestrian Detector in Multispectral Domain, Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras, EVReflex: Dense Time-To-Impact Prediction for Event-Based Obstacle Avoidance, PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds, A Registration-Aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition, INeRF: Inverting Neural Radiance Fields for Pose Estimation, RaP-Net: A Region-Wise and Point-Wise Weighting Network to Extract Robust Features for Indoor Localization, Differentiable Factor Graph Optimization for Learning Smoothers, Attention Augmented ConvLSTM for Environment Prediction, Overcoming Obstructions Via Bandwidth-Limited Multi-Agent Spatial Handshaking, Scene Descriptor Expressing Ambiguity in Information Recovery Based on Incomplete Partial Observation, Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation, Automatic Learning System for Object Function Points from Random Shape Generation and Physical Validation, Fast Image-Anomaly Mitigation for Autonomous Mobile Robots, Visual Identification of Articulated Object Parts, Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints, ViNet: Pushing the Limits of Visual Modality for Audio-Visual Saliency Prediction, MDN-VO: Estimating Visual Odometry with Confidence, Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments, Correlate-And-Excite: Real-Time Stereo Matching Via Guided Cost Volume Excitation, Improving Robot Localisation by Ignoring Visual Distraction, Semantic Segmentation-Assisted Scene Completion for LiDAR Point Clouds, Dynamic Domain Adaptation for Single-View 3D Reconstruction, You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module, VIPose: Real-Time Visual-Inertial 6D Object Pose Tracking, Using Visual Anomaly Detection for Task Execution Monitoring, Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes, Pose Estimation from RGB Images of Highly Symmetric Objects Using a Novel Multi-Pose Loss and Differential Rendering, Denoising 3D Human Poses from Low-Resolution Video Using Variational Autoencoder, KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation, All Characteristics Preservation: Single Image Dehazing Based on Hierarchical Detail Reconstruction Wavelet Decomposition Network, PCTMA-Net: Point Cloud Transformer with Morphing Atlas-Based Point Generation Network for Dense Point Cloud Completion, Superline: A Robust Line Segment Feature for Visual SLAM, ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images, Model Adaptation through Hypothesis Transfer with Gradual Knowledge Distillation, VoluMon: Weakly Supervised Volumetric Monocular Estimation with Ellipsoid Representations, Cross-Modal Representation Learning for Lightweight and Accurate Facial Action Unit Detection, Stereo Matching by Self-Supervision of Multiscopic Vision, Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation, Efficient Learning of Goal-Oriented Push-Grasping Synergy in Clutter, Iterative Coarse-To-Fine 6D-Pose Estimation Using Back-Propagation, Understanding Human Manipulation with the Environment: A Novel Taxonomy for Video Labelling, Excavation Learning for Rigid Objects in Clutter, Fast-Learning Grasping and Pre-Grasping Via Clutter Quantization and Q-Map Masking, Joint Space Control Via Deep Reinforcement Learning, Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers, Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter, Double-Dot Network for Antipodal Grasp Detection, Neural Motion Prediction for In-Flight Uneven Object Catching, Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation, Occlusion-Aware Search for Object Retrieval in Clutter, Grasp Pose Detection from a Single RGB Image, DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping, Reactive Long Horizon Task Execution Via Visual Skill and Precondition Models, Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3), DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration, Graph-Based Task-Specific Prediction Models for Interactions between Deformable and Rigid Objects, GhostPose*: Multi-View Pose Estimation of Transparent Objects for Robot Hand Grasping, Reinforcement Learning for Vision-Based Object Manipulation with Non-Parametric Policy and Action Primitives, Casting Manipulation of Unknown String by Robot Arm, Deformation Control of a Deformable Object Based on Visual and Tactile Feedback, A Soft Robotic Gripper with an Active Palm and Reconfigurable Fingers for Fully Dexterous In-Hand Manipulation, The Stewart Hand: A Highly Dexterous 6-Degrees-Of-Freedom Manipulator Based on the Stewart-Gough Platform (I), Real-Time Safety and Control of Robotic Manipulators with Torque Saturation in Operational Space, Robot Hand Based on a Spherical Parallel Mechanism for Within-Hand Rotations about a Fixed Point, Learning Compliant Grasping and Manipulation by Teleoperation with Adaptive Force Control, Optimal Scheduling and Non-Cooperative Distributed Model Predictive Control for Multiple Robotic Manipulators, OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation, Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots, Learning Connectivity for Data Distribution in Robot Teams, Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments, Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells, Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking, State Estimation and Model-Predictive Control for Multi-Robot Handling and Tracking of AGV Motions Using IGPS, Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks, Assembly Sequence Generation for New Objects Via Experience Learned from Similar Object, Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks, Learn to Differ: Sim2Real Small Defection Segmentation Network, Control Strategy for Jam and Wedge-Free 3D Precision Insertion of Heavy Objects Suspended with a Multi-Cable Crane, Combining Unsupervised Muscle Co-Contraction Estimation with Bio-Feedback Allows Augmented Kinesthetic Teaching, 3D Reactive Control and Frontier-Based Exploration for Unstructured Environments, Adaptive Terrain Traversability Prediction Based on Multi-Source Transfer Gaussian Processes, Multiclass Terrain Classification Using Sound and Vibration from Mobile Robot Terrain Interaction, Perceptive Autonomous Stair Climbing for Quadrupedal Robots, Trajectory Selection for Power-Over-Tether Atmospheric Sensing UAS, CCRobot-IV: An Obstacle-Free Split-Type Quad-Ducted Propeller-Driven Bridge Stay Cable-Climbing Robot, An Industrial Robot for Firewater Piping Inspection and Mapping, A Mixed Reality Supervision and Telepresence Interface for Outdoor Field Robotics, A Soft Somesthetic Robotic Finger Based on Conductive Working Liquid and an Origami Structure, Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects, AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection, A Low-Cost Modular System of Customizable, Versatile, and Flexible Tactile Sensor Arrays, Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration, A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration, A Multi-Axis FBG-Based Tactile Sensor for Gripping in Space, Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace, High Dynamic Range 6-Axis Force Sensor Employing a Semiconductor-Metallic Foil Strain Gauge Combination, Tactile Scanning for Detecting Micro Bump by Strain-Sensitive Artificial Skin, A Force Recognition System for Distinguishing Click Responses of Various Objects, A Robust Controller for Stable 3D Pinching Using Tactile Sensing, Dynamic Modeling of Hand-Object Interactions Via Tactile Sensing, A Local Filtering Technique for Robot Skin Data, Energy Generating Electronic Skin with Intrinsic Tactile Sensing without Touch Sensors (I), Sensor Selection for Detecting Deviations from a Planned Itinerary, Autonomous Decision-Making with Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning, Automata-Based Optimal Planning with Relaxed Specifications, Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learning, Learning from Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains, Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots, A Topological Approach to Finding Coarsely Diverse Paths, Probabilistic Specification Learning for Planning with Safety Constraints, Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic, Safe Linear Temporal Logic Motion Planning in Dynamic Environments, Decentralized Classification with Assume-Guarantee Planning, Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference, Formalizing the Execution Context of Behavior Trees for Runtime Verification of Deliberative Policies, Probabilistic Trajectory Prediction with Structural Constraints, Formalizing Trajectories in Human-Robot Encounters Via Probabilistic STL Inference, Convex Approximation for LTL-Based Planning, Temporal Force Synergies in Human Grasping, Trajectory-Based Split Hindsight Reverse Curriculum Learning, Detecting Grasp Phases and Adaption of Object-Hand Interaction Forces of a Soft Humanoid Hand Based on Tactile Feedback, SpectGRASP: Robotic Grasping by Spectral Correlation, Assessing Grasp Quality Using Local Sensitivity Analysis, Geometry-Based Grasping Pipeline for Bi-Modal Pick and Place, Computing a Task-Dependent Grasp Metric Using Second-Order Cone Programs, Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp, PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning, Geometric Characterization of the Planar Multi-Finger Equilibrium Grasps, Formulation and Validation of an Intuitive Quality Measure for Antipodal Grasp Pose Evaluation, Scooping Manipulation Via Motion Control with a Two-Fingered Gripper and Its Application to Bin Picking, DDGC: Generative Deep Dexterous Grasping in Clutter, Planning Grasps with Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes (I), A Three-Fingered Adaptive Gripper with Multiple Grasping Modes, Dexterous Textile Manipulation Using Electroadhesive Fingers, A Series Elastic, Compact Differential Mechanism: On the Development of Adaptive, Lightweight Robotic Grippers and Hands, Computational Design of Reconfigurable Underactuated Linkages for Adaptive Grippers, A Multi-Modal Robotic Gripper with a Reconfigurable Base: Improving Dexterous Manipulation without Compromising Grasping Efficiency, Grasping with Embedded Synergies through a Reconfigurable Electric Actuation Topology, An Under-Actuated Whippletree Mechanism Gripper Based on Multi-Objective Design Optimization with Auto-Tuned Weights, A Caging Inspired Gripper Using Flexible Fingers and a Movable Palm, The Role of Digit Arrangement in Soft Robotic In-Hand Manipulation, A Dexterous, Reconfigurable Robot Hand Combining Anthropomorphic and Interdigitated Configurations, A Computational Framework for Robot Hand Design Via Reinforcement Learning, Variable-Grasping-Mode Gripper with Different Finger Structures for Grasping Small-Sized Items, Force Control with Friction Compensation in a Pneumatic Gripper, Analysis of Fingertip Force Vector for Pinch-Lifting Gripper with Robust Adaptation to Environments (I), Design and Validation of a Smartphone-Based Haptic Feedback System for Gait Training, Robotic Guidance System for Visually Impaired Users Running Outdoors Using Haptic Feedback, Variable Stiffness Folding Joints for Haptic Feedback. hhJOyh, fHSZ, TYS, wsjwnT, cTosi, Eyr, FgGHZM, kTq, gWeoU, uDjs, 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