Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (11): 3221-3228.doi: 10.13229/j.cnki.jdxbgxb.20220976

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Detection and processing algorithm of slope point cloud in obstacle detection

Lin JIANG1,2(),Li YANG1,Wen-jun ZHANG3,Qiong-yu ZHANG3,Yan-xia WU3   

  1. 1.Key Laboratory of Metallurgical Equipment and Control Technology，Ministry of Education，Wuhan University of Science and Technology，Wuhan 430081，China
    2.Institute of Robotics and Intelligent Systems，Wuhan University of Science and Technology，Wuhan 430081，China
    3.Leador Spatial Information Technology Co. ，Ltd. ，Wuhan 430073，China
  • Received:2022-08-05 Online:2023-11-01 Published:2023-12-06

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Abstract:

When the outdoor mobile robot detects obstacles in the environment including sloping roads， the traditional RANSAC ground point cloud removal algorithm will be unable to remove the slope point clouds， resulting in the subsequent identification of the slope as obstacles. In view of this situation， this paper proposes an Adjacent Laser Points Algorithm to detect and eliminate slope point clouds， and detect obstacles based on Euclidean clustering. In this scheme， the 3D laser point cloud is preprocessed， and the ground point cloud including the slope is segmented according to the geometric relationship between the adjacent lines of the 3D lidar， and then downsampled by voxel filtering， the segmented obstacle point cloud is clustered based on KDTree， and the size and direction of the outer bounding frame are calculated by PCA principal component analysis to detect the obstacle. The experimental results show that the obstacle detection algorithm proposed in this paper can effectively segment the slope road surface in the environment， and can avoid identifying the slope road surface as an obstacle in the clustering process， which provides the basis for the robot autonomous walking obstacle avoidance strategy.

Key words: mechatronics, obstacle detection, adjacent laser points, euclidean cluster, slope road surface, principal component analysis of PCA

CLC Number: 

  • TP391.9

Fig.1

Simulation robot platform"

Fig.2

Real robot platform"

Fig.3

Eliminating point clouds on smooth ground with RANSAC algorithm"

Fig.4

Eliminating point clouds on slope with RANSAC algorithm"

Fig.5

Schematic diagram of algorithm"

Fig.6

Flow chart based on adjacent laser points algorithm"

Fig.7

Comparison of experiments in three scenarios"

Fig.8

Three-dimensional KDTree"

Fig.9

Flow chart of european clustering algorithm based on KDTree"

Fig.10

Schematic diagram of oriented bounding box"

Fig.11

Experimental results of obstacle extraction scheme"

Fig.12

Obstacle detection in general scenarios"

Table 1

Quantitative data analysis"

方案实时性/ms斜坡点云的检出率/%障碍物检测的准确率/%
传统6801073
使用本文算法50010089
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