{"title":"BiCR-SLAM: A multi-source fusion SLAM system for biped climbing robots in truss environments","authors":"Haifei Zhu, Jianhong Xu, Jingheng Chen, Shilang Chen, Yisheng Guan, Weinan Chen","doi":"10.1016/j.robot.2024.104685","DOIUrl":null,"url":null,"abstract":"<div><p>The low-texture, shape-similar, interconnected and mutual-occlusion nature of truss members poses challenges for simultaneous localization and mapping of biped climbing robots in truss environments. In this paper, we propose BiCR-SLAM, a multi-source fusion SLAM system, to estimate both the distinctive state of the robot and a parametric representation of the truss, going beyond traditional point cloud mapping. The proposed system comprises four modules such as encoder dead reckoning, LiDAR odometry, pole landmark mapping, and global optimization. To address the intricacies of truss environments, we present a pole landmark mapping module with dedicated operations including pole detection, data association, and parameterizations. In the back-end, we formulate the localization problem of biped climbing robots using a multi-source factor graph, encompassing factors including forward kinematics, LiDAR odometry, gripping, and points of poles. Experiments are conducted to evaluate the impact of various factors and to validate the effectiveness and accuracy of the proposed BiCR-SLAM system. A handheld LiDAR experiment in an outdoor large-scale truss environment demonstrates the generalization of our proposed approach.</p></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Autonomous Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092188902400068X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The low-texture, shape-similar, interconnected and mutual-occlusion nature of truss members poses challenges for simultaneous localization and mapping of biped climbing robots in truss environments. In this paper, we propose BiCR-SLAM, a multi-source fusion SLAM system, to estimate both the distinctive state of the robot and a parametric representation of the truss, going beyond traditional point cloud mapping. The proposed system comprises four modules such as encoder dead reckoning, LiDAR odometry, pole landmark mapping, and global optimization. To address the intricacies of truss environments, we present a pole landmark mapping module with dedicated operations including pole detection, data association, and parameterizations. In the back-end, we formulate the localization problem of biped climbing robots using a multi-source factor graph, encompassing factors including forward kinematics, LiDAR odometry, gripping, and points of poles. Experiments are conducted to evaluate the impact of various factors and to validate the effectiveness and accuracy of the proposed BiCR-SLAM system. A handheld LiDAR experiment in an outdoor large-scale truss environment demonstrates the generalization of our proposed approach.
期刊介绍:
Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems.
Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.