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ImMesh: An Immediate LiDAR Localization and Meshing Framework

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arxiv 2301.05206 v3 pith:IZQ5VGQL submitted 2023-01-12 cs.RO cs.CV

classification cs.ROcs.CV
keywords meshinglidarlocalizationtriangleframeworkimmeshmeshmodule
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In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mesh-Learner: Texturing Mesh with Spherical Harmonics

    cs.CV 2025-04 conditional novelty 6.0 of 10

    Mesh-Learner learns spherical-harmonic textures on each mesh triangle via a rasterization pipeline, achieving high-quality novel-view synthesis that is natively compatible with standard graphics software.

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