[1]WANG Xinjie,ZHANG Ying,ZHANG Dongbo,et al.Point cloud noise processing in path planning of autonomous mobile robot[J].CAAI Transactions on Intelligent Systems,2021,16(4):699-706.[doi:10.11992/tis.202007040]
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Point cloud noise processing in path planning of autonomous mobile robot

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 699-706 Column: 学术论文—智能系统 Public date: 2021-07-05
Title:
Point cloud noise processing in path planning of autonomous mobile robot
Author(s):
WANG Xinjie1;  ZHANG Ying1; 2;  ZHANG Dongbo1; 2;  WANG Yu1;  YANG Zhiqiao1
1. College of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
2. National Engineering Laboratory of Robot Vision Perception and Control Technology, Changsha 410082, China
Keywords:
point cloud map; pass through filtering; voxel filtering; statistical filtering; point cloud segmentation; mobile robot; motion planning; RGB-D perception
CLC:
TP242
DOI:
10.11992/tis.202007040
Abstract:
In the process of robots’ autonomous navigation, the image processing algorithm analyzes the 3D point cloud plane angles of obstacles in the path and calculates their height in real time, thus determining whether the robots can pass or not. However, a large amount of noise is present in the original point cloud data output by the RGB-D camera, thereby seriously affecting the accuracy of segmentation; such noise thus needs to be filtered. This paper first obtains the best parameters (K=20 and α=2) by analyzing and comparing the denoising effects of different parameters in statistical filtering in the Table Scene dataset. The mobile robot constructs a point cloud map in an outdoor environment through the ORB-SLAM2 algorithm and then performs passthrough filtering, voxel filtering, statistical filtering, and plane segmentation, calculating the slope angle and implementing motion planning. Experimental results show that the optimal statistical filter parameters obtained in the Table Scene dataset can be applied to outdoor environments and that robots can automatically plan their paths according to the calculation results and complete their specified tasks.
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