Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning

Yanze Jiang; Jianfeng Sun; Yuanxue Ding; Peng Jiang; Hailong Zhang; Sining Li; Shuaijun Zhou

doi:10.1117/12.2650864

10 May 2023 Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning

Yanze Jiang, Jianfeng Sun, Yuanxue Ding, Peng Jiang, Hailong Zhang, Sining Li, Shuaijun Zhou

Proceedings Volume 12554, AOPC 2022: Advanced Laser Technology and Applications; 1255404 (2023) https://doi.org/10.1117/12.2650864
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm- APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification dataset.Following that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research.

Citation Download Citation

Yanze Jiang, Jianfeng Sun, Yuanxue Ding, Peng Jiang, Hailong Zhang, Sining Li, and Shuaijun Zhou "Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning", Proc. SPIE 12554, AOPC 2022: Advanced Laser Technology and Applications, 1255404 (10 May 2023); https://doi.org/10.1117/12.2650864

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KEYWORDS

LIDAR

Information fusion

Image fusion

Data modeling

Data conversion

Image classification

Image processing

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