| Citation: | NIU Guochen, WANG Yu. Unmanned vehicle positioning and mapping method based on multi-constraint factor graph optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 306-314. doi: 10.13700/j.bh.1001-5965.2020.0212(in Chinese)
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Aimed at the problem that the current low-speed positioning system of unmanned vehicle extremely relies on the Global Navigation Satellite System (GNSS), which has low positioning accuracy, large drift error and serious environmental impact, a low-cost and high-precision positioning and mapping method is proposed. This method is based on the three-dimensional laser Simultaneous Localization and Mapping(SLAM) technology. First, the point cloud Principal Component Analysis (PCA) is used to implement laser odometry based on feature matching. Then, the GNSS location information, ground plane and clustering feature of point cloud obtained by point cloud segmentation and clustering are added to the graph optimization framework as pose constraints, and the cumulative error of the laser odometry is eliminated. Finally, an optimal pose and large-scale scenes point cloud map is obtained to achieve the unmanned vehicles' position navigation. The proposed SLAM algorithm is evaluated using the KITTI dataset containing large outdoor urban street environments. The results show that the positioning deviation of this system can be controlled below 1.5 m at a movement distance of 3 km, and both in terms of local accuracy and global consistency, it is superior to other odometry systems and provides new ideas for the positioning of unmanned vehicles.
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