Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs | |
Wang HQ(王瀚琪)2,3; Wang ZL(王智灵)1,2,4; Lin LL(林玲龙)1,2,4; Xu FY(徐凤煜)2,3; Yu J(余结)1,2,4; Liang HW(梁华为)1,2,4 | |
刊名 | Remote Sensing |
2021-10-14 | |
关键词 | autonomous vehicles vehicle pose estimation time and spatial dimensions 3D LiDAR |
英文摘要 | Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle’s optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate. |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/125908] |
专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
通讯作者 | Wang ZL(王智灵) |
作者单位 | 1.中国科学院机器人与智能制造创新研究院 2.中科学院合肥物质科学研究院 3.安徽省智能驾驶技术及应用工程实验室 4.中国科学技术大学 |
推荐引用方式 GB/T 7714 | Wang HQ,Wang ZL,Lin LL,et al. Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs[J]. Remote Sensing,2021. |
APA | Wang HQ,Wang ZL,Lin LL,Xu FY,Yu J,&Liang HW.(2021).Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs.Remote Sensing. |
MLA | Wang HQ,et al."Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs".Remote Sensing (2021). |
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