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arXiv · 2406.02096

MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection

Xiangcheng Hu·Jin Wu·Jianhao Jiao·Wei Zhang·Ping Tan
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Abstract

Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available.

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Xiangcheng Hu, Jin Wu, Jianhao Jiao, Wei Zhang, Ping Tan. 2024-06-04. MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection. https://arxiv.org/abs/2406.02096

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