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第二十九届中国控制会议

作者： ZHANG Heng,CUI Pin,LIU Yanli School of Information Engineering,East China Jiaotong University,Nanchang 330013,P.R.China

Start from point-to-point corresponding method,the scan matching problem for 2D laser measurement data association is studied.A relative robot displacement estimation method based on curve mass centre correspondence（CMCC）is *** show that this method is easy to realize and converges faster than the iterative closest point（ICP）method,but its precision is not *** the CMCC method and ICP method are used together,the matching speed is raised and the precision is also ensured.

关键词： Mobile Robot Displacement Estimation scan matching

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Grid graph-based large-scale point clouds registration

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International Journal of Digital Earth 2023年第1期16卷 2448-2466页

作者： Yi Han Guangyun Zhang Rongting Zhang School of Geomatics Science and Technology Nanjing Tech UniversityNanjingPeople's Republic of China

Automatic registration of unordered point clouds is the prerequisite forurban reconstruction. However, most of the existing technologies stillsuffer from some limitations. On one hand, most of them are sensitive tonoise and repetitive structures, which makes them infeasible for theregistration of large-scale point clouds. On the other hand, most of themdealing with point clouds with limited overlaps and unpredictablelocation. All these problems make it difficult for registration technology toobtain qualified results in outdoor point cloud. To overcome theselimitations, this paper presents a grid graph-based point cloud registration(GGR) algorithm to align pairwise scans. First, point cloud is divided into aset of 3D grids. We propose a voting strategy to measure the similaritybetween two grids based on feature descriptor, transforming thesuperficial correspondence into 3D grid expression. Next, a graphmatching is proposed to capture the spatial consistency from putativecorrespondences, and graph matching hierarchically refines thecorresponding grids until obtaining point-to-point *** experiments demonstrated that the proposed algorithmobtains good performance in terms of successful registration rate, rotationerror, translation error, and outperformed the state-of-the-art approaches.

关键词： Point cloud alignment scan matching graph algorithms reconstruction

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Map-based Robust Localization for Indoor Mobile Robots

Map-based Robust Localization for Indoor Mobile Robots

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第36届中国控制会议

作者： SONG Jianchao ZHANG Xuebo SUN Lei LIU Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics

ISBN: (纸本)9781538629185

Standard Monte-Carlo Localization（MCL） cannot work well in dynamic environments and it usually requires a large number of particles to obtain stable and accurate pose *** cope with these issues,this paper proposes an improved MCL approach inspired by motion detection and scan matching for robust localization of indoor mobile *** from standard MCL,the proposed approach incorporates a motion detection module to handle dynamic environments,and it also considers the ray tracing characteristics of each laser beam to optimize the particle *** proposed approach is implemented under Robot Operating System（ROS）,and comparative experimental results are provided to illustrate the superior performance of the proposed approach.

关键词： Monte-Carlo Localization Motion Detection scan matching Indoor Environment Robot Operating System

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An Odometry Estimation Method for Micro Indoor Flying Robot with Lightweight Sensors

An Odometry Estimation Method for Micro Indoor Flying Robot ...

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第25届中国控制与决策会议

作者： Zheng Fang Lei Zhang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

ISBN: (纸本)9781467355339

Micro-quadrotor usually equips with lightweight sensors due to its limited payload. In this paper, an odometry estimation method based on 2D laser scanner and monocular vision is proposed to solve the pose estimation problem of micro-quadrotor flying robot in indoor environments. This method firstly detects and matches image features of current and previous frames to get all feature correspondences to calculate the monocular odometry. Then, laser scans are transformed into image coordinate system using sensor calibration parameters. After that, the unknown translation scale of monocular odometry is determined by using triangulation. At the same time, line features of laser scans are extracted and the line angle histogram is computed. If the angle distribution is wide enough, which means it is likely to be in a structural environment, a scan matching algorithm is used to calculate the laser odometry. Finally, estimation results based on the two different sensors are fused by using weighted average method. Experiment results show that the proposed method can recover the trajectory of the robot robustly and accurately. The close loop error is less than 1% for a 64 m loop around indoor corridor.

关键词： Monocular Visual Odometry scan matching Pose estimation Sensor Fusion

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