LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry

Garcia-Fidalgo, Emilio; Company-Corcoles, Joan P.; Bonnín-Pascual, Francisco; Ortíz, Alberto

doi:10.48550/arxiv.2111.03393

Cited by 1 publication

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“…The edge and plane features of the point cloud can realize the abstraction of data, which is proposed by Zhang et al, and based on this, a real-time LiDAR odometry and mapping method [9][10][11][12] is also proposed, named LOAM. They first extract the edge and planer features of the LiDAR scans by calculating the smoothness of the point by comparing the distance with its neighbor, and then associate the feature between the adjacent frames and construct the error function that to be solution.…”

Section: Related Workmentioning

confidence: 99%

Segmentation based lidar odometry and mapping

Wang

Cao²,

Zhang³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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LiDAR based Simultaneous Localization and Mapping (LiDAR SLAM) plays a vital role in autonomous driving and has attracted the attention of researchers. In order to achieve higher accuracy of motion estimation between adjacent LiDAR frames and reconstruction of the map, a segmentation-based LiDAR odometry and mapping framework is proposed in this paper. In detail, we first define the classification of several features with weak semantic information, the extraction method of which is achieved by a segmentation algorithm proposed in this paper that is based on greedy search. Based on the above work, a novel point cloud registration algorithm is also proposed in this paper, which is solved by modeling the problem as a nonlinear optimization problem. In order to verify the effectiveness of the proposed model, we collect a large amount of data in the autonomous driving test area to test it and compare the results with the existing state-of-the-art models. The experimental results show that the algorithm proposed in this paper can run stably in real-world autonomous driving scenarios and has smaller error and higher robustness compared with other models.

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Section: Related Workmentioning

confidence: 99%