Algorithmic validation demonstrates improved denoising accuracy and reduced false deletions in sparse LiDAR data, indicating superior real-time boundary retention.
This study proposes a Geometric-consistency-based Weighted Adaptive Fusion (GWAF) algorithm for denoising sparse LiDAR point clouds. The method integrates four key parameters, distance, angular continuity, ring number, and reflection intensity, into a unified geometric-weight system. This integration facilitates both adaptive neighborhood construction and dynamic thresholding. By establishing an intensity-radius mapping and applying multi-view consistency, the GWAF algorithm enhances boundary retention and weak target detection. Experimental results, using data from a RoboSense M1 LiDAR, demonstrate that the algorithm achieves a 2.4% improvement in denoising accuracy (0.8888) and a 15% reduction in false deletions. These performance gains surpass those of traditional radius and statistical filters, all while maintaining real-time efficiency.
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Zhang et al. (2026) studied this question.
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