Outdoor environment segmentation and obstacle detection

Chen Hao; Yuanhong Dan

doi:10.1117/12.3029693

21 June 2024 Outdoor environment segmentation and obstacle detection

Chen Hao, Yuanhong Dan

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671J (2024) https://doi.org/10.1117/12.3029693
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Aiming at the problem that the current laser point cloud obstacle detection calculation method has poor segmentation effect in the outdoor volcanic environment, and the time length and obstacle error detection rate are high, a partition method that can automatically evaluate the sensor height and improve the Euclidean partition method is planned. Clustering algorithm obstacle detection strategy. Firstly, the ground simulation is carried out through the segmentation method of sub-regions, and on this basis, the method of automatic recognition of the sensor height is added to solve the problem of insufficient segmentation of outdoor slopes; then, the improved version of the Euclidean aggregation algorithm is used to obtain The off-site points of the object are aggregated and framed for visualization; finally, the experimental verification is carried out on the collected user external environment data set on the real experimental platform. The experimental results show that: compared with the traditional plane simulation combined with the ground plane segmentation algorithm and the traditional fixed value Feng family algorithm, the should segmentation method and detection algorithm are ab out 16% shorter than the time cost reduction, and the positive inspection rate is about 24% higher.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chen Hao and Yuanhong Dan "Outdoor environment segmentation and obstacle detection", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671J (21 June 2024); https://doi.org/10.1117/12.3029693

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KEYWORDS

Point clouds

Detection and tracking algorithms

Sensors

Tolerancing

LIDAR

Tunable filters

Principal component analysis

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