IM2DP: an intensity-based approach to loop closure detection and optimization for LiDAR mapping

Lu Qiang; Jiahang Liu

doi:10.1117/12.2674664

23 May 2023 IM2DP: an intensity-based approach to loop closure detection and optimization for LiDAR mapping

Lu Qiang, Jiahang Liu

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126040H (2023) https://doi.org/10.1117/12.2674664
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

We propose a brand-new global point cloud descriptor called IM2DP that combines shape and intensity data to conduct loop closure detection. By incorporating intensity data, our method expands the multiview 2D projection (M2DP) descriptor. We calculate intensity characteristics from multiple 2D projections of the point cloud and apply them to the M2DP shape features. Then, singular value decomposition (SVD) is used to compute the compressed descriptors for reducing the dimensionality. We combine this algorithm with the LOAM and conduct experiments on the KITTI public dataset. The results show that our approach is effective in reducing trajectory error and shows competitiveness when compared to the M2DP.

Citation Download Citation

Lu Qiang and Jiahang Liu "IM2DP: an intensity-based approach to loop closure detection and optimization for LiDAR mapping", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126040H (23 May 2023); https://doi.org/10.1117/12.2674664

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KEYWORDS

Point clouds

LIDAR

Matrices

Histograms

Mathematical optimization

Singular value decomposition

Signal intensity

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