Paper
10 August 2023 A novel global localization algorithm based on ring-wise encoding descriptors
Yongzhe Chen, Xiaomeng Wei, Gang Wang, Wei Zhou
Author Affiliations +
Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127591V (2023) https://doi.org/10.1117/12.2686517
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China
Abstract
The map-based global localization technique leverages global descriptors to articulate overall features of point clouds, and facilitates vehicle localization through comparison with the map keyframe. However, this technique is capable of facing challenges when there is an offset between the vehicle position and map keyframes, making it difficult to achieve a match and thereby adversely affecting the accuracy and robustness of the localization system. To mitigate this challenge, this paper presents a novel approach for global localization that designs ring-wise encoding descriptors. This approach involves the integration of ring-wise elevation difference encoding and the shifting of the point cloud coordinate system to construct global descriptors that possesses rotational and translational invariance, thereby enabling rough localization. Additionally, the search range is dynamically adjusted established upon the similarity score of descriptors, and fine localization is achieved employing the branch and bound scan matching algorithm. The presented approach is validated on the KITTI dataset and JLU Campus dataset. Results demonstrate a high success rate in global localization, as well as excellent generalization and practicality.
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Yongzhe Chen, Xiaomeng Wei, Gang Wang, and Wei Zhou "A novel global localization algorithm based on ring-wise encoding descriptors", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127591V (10 August 2023); https://doi.org/10.1117/12.2686517
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KEYWORDS
Point clouds

LIDAR

Lab on a chip

Semantics

Autonomous driving

Data modeling

Nondestructive evaluation

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