A deep learning solution for compressed semantic segmentation of LiDAR point cloud maps
DOI:
https://doi.org/10.54939/1859-1043.j.mst.IITE.2025.131-138Keywords:
Deep learning, Localization and navigation; Point cloud; LiDAR; Semantic segmentation.Abstract
Navigating Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments often relies on pre-built Light Detection and Ranging (LiDAR) maps. However, the large memory footprint and high computational cost of these point cloud maps pose significant challenges for resource-constrained UAVs. This paper proposes a deep learning solution using a lightweight, modified RandLA-Net architecture to efficiently compress and semantically segment these maps. Our results demonstrate a significant reduction in model size and memory usage while maintaining competitive segmentation accuracy, presenting a viable solution for real-time, on-board processing on embedded systems.
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