2021 IEEE International Conference on Robotics and Automation (ICRA)最新文献

Star Topology based Interaction for Robust Trajectory Forecasting in Dynamic Scene 基于星型拓扑的动态场景鲁棒轨迹预测

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561067

Yanliang Zhu, Dongchun Ren, Deheng Qian, Mingyu Fan, Xin Li, Huaxia Xia

{"title":"Star Topology based Interaction for Robust Trajectory Forecasting in Dynamic Scene","authors":"Yanliang Zhu, Dongchun Ren, Deheng Qian, Mingyu Fan, Xin Li, Huaxia Xia","doi":"10.1109/ICRA48506.2021.9561067","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561067","url":null,"abstract":"Motion prediction of multiple agents in a dynamic scene is a crucial component in many real applications, including intelligent monitoring and autonomous driving. Due to the complex interactions among the agents and their interactions with the surrounding scene, accurate trajectory prediction is still a great challenge. In this paper, we propose a new method for robust trajectory prediction of multiple intelligent agents in a dynamic scene. The input of the method includes the observed trajectories of all agents, and optionally, the planning of the ego-agent and the surrounding high definition map at every time steps. Given observed trajectories, an efficient approach in a star computational topology is utilized to compute both the spatiotemporal interaction features and the current interaction features between the agents, where the time complexity scales linearly to the number of agents. Moreover, on an autonomous vehicle, the proposed prediction method can make use of the planning of ego-agent to improve the modeling of the interaction between surrounding agents. To increase the robustness to upstream perception noises, at the training stage, we randomly mask out the input data, a.k.a. the points on the observed trajectories of agents and the lane sequence. Experiments on autonomous driving and pedestrian-walking datasets demonstrate that the proposed method is not only effective when the planning of ego-agent and the high definition map are provided, but also achieves state-of-the-art performance with only the observed trajectories.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"10 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":"115375236","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

Occupancy Map Inpainting for Online Robot Navigation 基于在线机器人导航的占用地图绘制

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561790

Minghan Wei, Daewon Lee, Volkan Isler, Daniel D. Lee

{"title":"Occupancy Map Inpainting for Online Robot Navigation","authors":"Minghan Wei, Daewon Lee, Volkan Isler, Daniel D. Lee","doi":"10.1109/ICRA48506.2021.9561790","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561790","url":null,"abstract":"In this work, we focus on mobile robot navigation in indoor environments where occlusions and field-of-view limitations hinder onboard sensing capabilities. We show that the footprint of a camera mounted on a robot can be drastically improved using learning-based approaches. Specifically, we consider the task of building an occupancy map for autonomous navigation of a robot equipped with a depth camera. In our approach, a local occupancy map is first computed using measurements from the camera directly. Afterwards, an inpainting network adds further information, the occupancy probabilities of unseen grid cells, to the map. A novel aspect of our approach is that rather than direct supervision from ground truth, we combine the information from a second camera with a better field-of-view for supervision. The training focuses on predicting extensions of the sensed data. To test the effectiveness of our approach, we use a robot setup with a single camera placed at 0.5m above the ground. We compare the navigation performance using raw maps from only this camera’s input (baseline) versus using inpainted maps augmented with our network. Our method outperforms the baseline approach even in completely new environments not included in the training set and can yield 21% shorter paths than the baseline approach. A real-time implementation of our method on a mobile robot is also tested in home and office environments.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"11 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":"115650951","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: 8

A Variable Stiffness Actuator Based on Second-order Lever Mechanism and Its Manipulator Integration 基于二阶杠杆机构的变刚度作动器及其机械手集成

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561932

Zhangxing Liu, Hongzhe Jin, Hui Zhang, Yubin Liu, Yilin Long, Xiufang Liu, Jie Zhao

引用次数: 4

Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds 概率扫描匹配:基于点云的贝叶斯姿态估计

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561979

Rico Mendrzik, Florian Meyer

{"title":"Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds","authors":"Rico Mendrzik, Florian Meyer","doi":"10.1109/ICRA48506.2021.9561979","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561979","url":null,"abstract":"Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point clouds coincide. The association of measurements in point cloud one with measurements in point cloud two is a problem inherent to scan matching. Existing methods perform non-probabilistic data association, i.e., they assume a single association hypothesis. This leads to overconfident pose estimates and reduced estimation accuracy in ambiguous environments. Our probabilistic scan matching approach addresses this issue by considering all association hypotheses with their respective likelihoods. We formulate a holistic Bayesian estimation problem for both data association and pose inference and present the corresponding joint factor graph. Near-optimum maximum a posteriori (MAP) estimates of the sensor pose are computed by performing iterative message passing on the factor graph. Our numerical study shows performance improvements compared to non-probabilistic scan matching methods that are based on the normal distributions transform (NDT) and implicit moving least squares (IMLS).","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"19 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":"125215227","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: 0

Simultaneous Precision Assembly of Multiple Objects through Coordinated Micro-robot Manipulation 基于微机器人协调操作的多目标同步精密装配

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561293

Song Liu, Yuyu Jia, Youfu Li, Yao Guo, Haojian Lu

引用次数: 1

Stereo-augmented Depth Completion from a Single RGB-LiDAR image 单幅RGB-LiDAR图像的立体增强深度补全

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561557

Keunhoon Choi, Somi Jeong, Youngjung Kim, K. Sohn

{"title":"Stereo-augmented Depth Completion from a Single RGB-LiDAR image","authors":"Keunhoon Choi, Somi Jeong, Youngjung Kim, K. Sohn","doi":"10.1109/ICRA48506.2021.9561557","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561557","url":null,"abstract":"Depth completion is an important task in computer vision and robotics applications, which aims at predicting accurate dense depth from a single RGB-LiDAR image. Convolutional neural networks (CNNs) have been widely used for depth completion to learn a mapping function from sparse to dense depth. However, recent methods do not exploit any 3D geometric cues during the inference stage and mainly rely on sophisticated CNN architectures. In this paper, we present a cascade and geometrically inspired learning framework for depth completion, consisting of three stages: view extrapolation, stereo matching, and depth refinement. The first stage extrapolates a virtual (right) view using a single RGB (left) and its LiDAR data. We then mimic the binocular stereo-matching, and as a result, explicitly encode geometric constraints during depth completion. This stage augments the final refinement process by providing additional geometric reasoning. We also introduce a distillation framework based on teacher-student strategy to effectively train our network. Knowledge from a teacher model privileged with real stereo pairs is transferred to the student through feature distillation. Experimental results on KITTI depth completion benchmark demonstrate that the proposed method is superior to state-of-the-art methods.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"8 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":"117056638","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

Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance 在智能手机或机器人手持相机的徒手视频中进行健壮的皮肤特征跟踪，以实现临床工具定位和指导

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561616

Chun-Yin Huang, J. Galeotti

{"title":"Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance","authors":"Chun-Yin Huang, J. Galeotti","doi":"10.1109/ICRA48506.2021.9561616","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561616","url":null,"abstract":"Our novel skin-feature visual-tracking algorithm enables anatomic vSLAM and (by extension) localization of clinical tools relative to the patient’s body. Tracking naturally occurring features is challenging due to patient uniqueness, deformability, and lack of an accurate a-priori 3D geometric model. Our method (i) tracks skin features in a smartphone-camera video sequence, (ii) performs anatomic Simultaneous Localization And Mapping (SLAM) of camera motion relative to the patient’s 3D skin surface, and (iii) utilizes existing visual methods to track clinical tool(s) relative to the patient’s reconstructed 3D skin surface. (We demonstrate tracking of a simulated ultrasound probe relative to the patient by using an Apriltag visual fiducial). Our skin-feature tracking method utilizes the Fourier-Mellin Transform for robust performance, which we incorporated and extend an existing Phase Only Correlation (POC) based algorithm to be suitable for our application of free-hand smartphone video, wherein the distance of the camera fluctuates relative to the patient. Our SLAM approach further utilizes Structure from Motion and Bundle Adjustment to achieve an accurate 3D model of the human body with minimal drift-error in camera trajectory. We believe this to be the first freehand smartphone-camera tracking of natural skin features for anatomic tracking of surgical tools, ultrasound probe, etc.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"34 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":"117112563","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: 0

Chance Constrained Simultaneous Path Planning and Task Assignment with Bottleneck Objective 基于瓶颈目标的机会约束同步路径规划与任务分配

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561276

F. Yang, N. Chakraborty

{"title":"Chance Constrained Simultaneous Path Planning and Task Assignment with Bottleneck Objective","authors":"F. Yang, N. Chakraborty","doi":"10.1109/ICRA48506.2021.9561276","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561276","url":null,"abstract":"We present a novel algorithm for combined task assignment and path planning on a roadmap with stochastic costs. In this problem, the initially unassigned robots and tasks are located at known positions in a roadmap. We want to assign a unique task to each robot and compute a path for the robot to go to the task location. Given the means and variances of travel cost, our goal is to develop algorithms that guarantee that for each robot, with high probability, the total travel cost is below a minimum value in any realization of the stochastic travel costs. We prove that the solution can be obtained by solving (a) a chance-constrained shortest path problems for all robot-task pairs and (b) a linear bottleneck assignment problem in which the cost of an assignment is equal to the optimal objective value of the former problem. We propose algorithms for solving the chance-constrained shortest path problem either optimally or approximately by solving a number of deterministic shortest path problems that minimize some linear combination of means and variances of edge costs. We present simulation results on randomly generated networks and data to demonstrate that our algorithm is scalable with the number of robots (or tasks) and the size of the network.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"421 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":"117344450","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: 0

Semantic Feature Mining for 3D Object Classification and Segmentation 基于语义特征挖掘的三维目标分类与分割

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561986

Weihao Lu, Dezong Zhao, C. Premebida, Wen‐Hua Chen, Daxin Tian

{"title":"Semantic Feature Mining for 3D Object Classification and Segmentation","authors":"Weihao Lu, Dezong Zhao, C. Premebida, Wen‐Hua Chen, Daxin Tian","doi":"10.1109/ICRA48506.2021.9561986","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561986","url":null,"abstract":"Deep learning on 3D point clouds has drawn much attention, due to its large variety of applications in intelligent perception for automated and robotic systems. Unlike structured 2D images, it is challenging to extract features and implement convolutional networks over these unordered points. Although a number of previous works achieved high accuracies for point cloud recognition, they tend to process local point information in such a way that semantic information is not fully encoded. In this paper, we propose a deep neural network for 3D point cloud processing that utilizes effective feature aggregation methods emphasizing both generalizability and relevance. In particular, our method uses fixed-radius grouping for pooling layers and spherical kernel convolution for semantics mining. To address the issue of gradient degradation and memory consumption of a deep network, a parallel feature feed-forward mechanism and bottleneck layers are implemented to reduce the number of parameters. Experiments show that our algorithm achieves state-of-the-art results and competitive accuracy in both classification and part segmentation while maintaining an efficient architecture.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"47 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":"121081222","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: 1

LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation LiDARNet:一种边界感知的点云语义分割领域自适应模型

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561255

Peng Jiang, S. Saripalli

{"title":"LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation","authors":"Peng Jiang, S. Saripalli","doi":"10.1109/ICRA48506.2021.9561255","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561255","url":null,"abstract":"We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the domain shared and private features. Additionally, we introduce a new dataset (SemanticUSL1) for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. We conducted experiments on real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have differences in channel distributions, reflectivity distributions, diversity of scenes, and sensors setup. Using our approach, we can get a single projection-based Li-DAR full-scene semantic segmentation model working on both domains. Our model can keep almost the same performance on the source domain after adaptation and get an 8%-22% mIoU performance increase in the target domain.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"10 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":"127449322","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: 33