2021 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第6页

Tightly-Coupled Perception and Navigation of Heterogeneous Land-Air Robots in Complex Scenarios 复杂场景下异质陆空机器人的紧密耦合感知与导航

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9562042

Yufeng Yue, Mingxing Wen, Yosmar Putra, Meiling Wang, Danwei W. Wang

引用次数: 3

Automatic Hanging Point Learning from Random Shape Generation and Physical Function Validation 基于随机形状生成和物理功能验证的自动挂点学习

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561484

Kosuke Takeuchi, Iori Yanokura, Youhei Kakiuchi, K. Okada, M. Inaba

引用次数: 2

From Multi-Target Sensory Coverage to Complete Sensory Coverage: An Optimization-Based Robotic Sensory Coverage Approach 从多目标感官覆盖到完全感官覆盖:一种基于优化的机器人感官覆盖方法

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561213

J. Burdick, Amanda Bouman, E. Rimon

引用次数: 1

Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots 基于深度自编码器的移动操作机器人物体滑动感知多模态异常检测

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561586

Youngjae Yoo, Chung-yeon Lee, Byoung-Tak Zhang

{"title":"Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots","authors":"Youngjae Yoo, Chung-yeon Lee, Byoung-Tak Zhang","doi":"10.1109/ICRA48506.2021.9561586","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561586","url":null,"abstract":"Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms’ slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot’s movement in a changing environment. To solve this problem, we present an anomaly detection method that utilizes multisensory data based on a deep autoencoder model. The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder’s latent values and the latent values of reconstructed input data. In order to evaluate the proposed framework, we conducted an experiment that mimics an object slip by a mobile service robot operating in a real-world environment with diverse household objects and different moving patterns. The experimental results verified that the proposed framework reliably detects anomalies in object slip situations despite various object types and robot behaviors, and visual and auditory noise in the environment.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116208787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Fast Replanning Multi-Heuristic A * 快速重规划多启发式A *

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561928

Junhyoung Ha, Soonkyum Kim

{"title":"Fast Replanning Multi-Heuristic A *","authors":"Junhyoung Ha, Soonkyum Kim","doi":"10.1109/ICRA48506.2021.9561928","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561928","url":null,"abstract":"In this paper, we proposed a novel path replanning algorithm on arbitrary graphs. To avoid computationally heavy preprocessing and to reduce required memory to store the expanded vertices of the previous search, we defined the feature vertices, which are extracted from the previous path by a simple algorithm to compare the costs between adjacent vertices along the path once. Proper additional heuristic functions are designed for these feature vertices to work as local attractors guiding the search toward the previous path’s neighbors. To avoid unnecessary expansions and speed up the search, these additional heuristic functions are properly managed to stop intriguing or guiding search toward the feature vertices. The proposed algorithm of Fast Replanning Multi-Heuristic A* is a variation of Shared Multi-Heuristic A* while removing or deactivating the additional heuristic functions during the search. Fast Replanning Multi-Heuristic A* guarantees the bounded suboptimality while efficiently exploring the graph toward the goal vertex. The performance of the proposed algorithm was compared with weighted A* and D* lite by simulating numerous path replanning problems in maze-like maps.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121555151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Visual-Inertial Filtering for Human Walking Quantification 视觉惯性滤波用于人体步行量化

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561517

M. Mitjans, Michail Theofanidis, Ashley N. Collimore, Madelaine L. Disney, David M. Levine, L. Awad, Roberto Tron

{"title":"Visual-Inertial Filtering for Human Walking Quantification","authors":"M. Mitjans, Michail Theofanidis, Ashley N. Collimore, Madelaine L. Disney, David M. Levine, L. Awad, Roberto Tron","doi":"10.1109/ICRA48506.2021.9561517","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561517","url":null,"abstract":"We propose a novel system to track human lower-body motion as part of a larger movement assessment system for clinical evaluation. Our system combines multiple wearable Inertial Measurement Unit (IMU) sensors and a single external RGB-D camera. We use a factor graph with a Sliding Window Filter (SWF) formulation that merges 2-D joint data extracted from the RGB images via a Deep Neural Network, raw depth information, raw IMU gyroscope readings, and estimated foot contacts extracted from IMU gyroscope and accelerometer data. For the system, we use an articulated model of human body motion based on differential manifolds. We compare the results of our system against a gold-standard motion capture system and a vision-only alternative. Our proposed system qualitatively presents smoother 3D joint trajectories when compared to noisy depth data, allowing for more realistic gait estimations. At the same time, with respect to the vision-only baseline, it improves the median of the joint trajectories by around 2cm, while considerably reducing outliers by up to 0.6m.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robot-supervised Learning of Crop Row Segmentation* 作物行分割的机器人监督学习*

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560815

Marianne Bakken, Vignesh R. Ponnambalam, R. Moore, J. G. Gjevestad, P. From

引用次数: 5

Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board 利用任意平面板进行距离偏移校正的三维激光雷达的自动外部校准

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561175

Junha Kim, Changhyeon Kim, Young-Hwan Han, H. Kim

{"title":"Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board","authors":"Junha Kim, Changhyeon Kim, Young-Hwan Han, H. Kim","doi":"10.1109/ICRA48506.2021.9561175","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561175","url":null,"abstract":"This paper proposes an automatic and accuracy- enhanced extrinsic calibration method for 3D LiDARs with a range offset correction, which needs only an arbitrarily-shaped single planar board. One of the most exhaustive parts of existing LiDAR calibration procedures is to manually find target objects from massive point clouds. To obviate user interventions, we propose an automated planar board detection from LiDAR range images. To extract a target completely, we suppress outliers and restore rejected inliers of the target board by introducing a target completion method. We empirically find that range measurements of various LiDARs are mainly skewed by constant offset values. To compensate for this, we suggest a range offset model for each laser channel in calibration procedures. The relative pose between LiDARs and range offsets are jointly estimated by minimizing bi-directional point- to-board distances within the iterative re-weighted least squares (IRLS) framework. To verify the suggested range offset model, we obtain and analyze extensive real-world measurements. By conducting experiments using the various sensor configurations and shapes of boards, we quantitatively and qualitatively confirm accuracy and versatility of the proposed method by comparing with the state-of-the-art LiDAR calibration methods. All the source code and data used in the paper are available at : https://github.com/JunhaAgu/AutoL2LCalib.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"407 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System 基于r - cnn的新型自适应双模机器人锚定系统快速决策

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560735

Shahrooz Shahin, Rasoul Sadeghian, S. Sareh

引用次数: 3

Control of a Transfemoral Prosthesis on Sloped Terrain using Continuous and Nonlinear Impedance Parameters 利用连续和非线性阻抗参数控制经股骨假体在斜坡上的运动

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560910

N. A. Kumar, Woolim Hong, Pilwon Hur

引用次数: 2