Unmanned vehicle fusion positioning method based on 3D point cloud map and ESKF
| Autor: | CUI Wen, XUE Qiwen, LI Qingling, WANG Fengdong, HAO Xueer |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 48, Iss 9, Pp 116-122 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x |
| DOI: | 10.13272/j.issn.1671-251x.17997 |
| Popis: | The precision of the unmanned vehicle positioning method based on map matching depends on the precision of the created map. The precision is less affected by the outside world. The method is suitable for unmanned vehicle positioning in complex scenarios. However, the common laser radar point cloud matching algorithm takes single characteristic as the core for matching. The matching accuracy rate is low for large-scale point clouds. Therefore, the 3D point cloud map deviates greatly from the actual environment, resulting in the low precision of the unmanned vehicle positioning method based on map matching. To solve the above problems, an unmanned vehicle fusion positioning method based on 3D point cloud map and error state Kalman filter (ESKF) is proposed. The method is composed of two parts: 3D point cloud map construction and ESKF fusion positioning. In the 3D point cloud map construction part, inter-frame point cloud matching is performed through the normal distribution transform (NDT) algorithm to improve the accuracy of large-scale point cloud matching. The closed-loop constraint is added to construct graph optimization problem on the basis of the vertex and the constraint edge of the position and posture graph established by the laser odometer data. The Levenberg-Marquardt (LM) algorithm is used for the graph optimization solution in order to reduce the cumulative drift of the position and posture and improve the 3D point cloud map precision. In the ESKF fusion positioning part, ESKF is used to fuse inertial measurement unit (IMU) data and 3D point cloud map data. The correction of the prior position and posture (position, attitude and velocity) of the unmanned vehicle is realized and the posterior position and posture are output. The experimental results show that, compared with the method based on map matching, the maximum relative position and posture error, average error and root-mean-square error of the proposed method are reduced by 0.176 9 m, 0.027 1 m and 0.059 4 m respectively. The proposed method has better performance in positioning precision and stability. |
| Databáze: | Directory of Open Access Journals |
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