Intelligent Vehicle Localization Based on Polarized LiDAR Representation and Siamese Network

TAO Qianwen; HU Zhaozheng; WAN Jinjie; HU Huahua; ZHANG Ming

doi:10.11999/JEIT220140

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1163-1172

TAO Qianwen, HU Zhaozheng, WAN Jinjie, HU Huahua, ZHANG Ming. Intelligent Vehicle Localization Based on Polarized LiDAR Representation and Siamese Network[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1163-1172. doi: 10.11999/JEIT220140

Citation:

TAO Qianwen, HU Zhaozheng, WAN Jinjie, HU Huahua, ZHANG Ming. Intelligent Vehicle Localization Based on Polarized LiDAR Representation and Siamese Network[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1163-1172. doi: 10.11999/JEIT220140

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Intelligent Vehicle Localization Based on Polarized LiDAR Representation and Siamese Network

doi: 10.11999/JEIT220140

1.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
3.
Chongqing Research Institute of Wuhan University of Technology, Chongqing 401120, China

Funds: The Fundations of Wuhan Science and Technology Bureau (2020010601012165, 2020010602011973), The Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0978), The National Key Research and Development Program of China (2021YFB2501100)

Received Date: 2022-02-15
Rev Recd Date: 2022-06-13

Available Online: 2022-06-22

Publish Date: 2023-04-10

Abstract

Abstract

Intelligent vehicle localization based on 3D Light Detection And Ranging (LiDAR) is still a challenging task in map storage and the efficiency and accuracy of map matching. A lightweight node-level polarized LiDAR map is constructed by a series of nodes with a 2D polarized LiDAR image, a polarized LiDAR fingerprint, and sensor pose, while the polarized LiDAR image encodes a 3D cloud using a multi-channel image format, and the fingerprint is extracted and trained using Siamese network. An intelligent vehicle localization method is also proposed by matching with the polarized LiDAR map. Firstly, Siamese network is used to model the similarity of the query and map fingerprints for fast and coarse map matching. Then a Second-Order Hidden Markov Model (HMM2)-based map sequence matching method is used to find the nearest map node. Finally, the vehicle is readily localized using 3D registration. The proposed method is tested using the actual field data and the public KITTI database. The results indicate that the proposed method can achieve map matching accuracy up to 96% and 30cm localization accuracy with robustness in different types of LiDAR sensors and different environments.
- Intelligent vehicle localization,
- Polarized LiDAR representation,
- Map matching,
- Siamese network

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References(18)

References

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