Off-Road LiDAR Intensity Based Semantic Segmentation

• URL: http://arxiv.org/abs/2401.01439v1
• Date: Tue, 2 Jan 2024 21:27:43 GMT
• Title: Off-Road LiDAR Intensity Based Semantic Segmentation
• Authors: Kasi Viswanath, Peng Jiang, Sujit PB, Srikanth Saripalli
• Abstract summary: Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects in LiDAR point clouds. We address this problem by harnessing the LiDAR intensity parameter to enhance object segmentation in off-road environments. Our approach was evaluated in the RELLIS-3D data set and yielded promising results as a preliminary analysis with improved mIoU for classes "puddle" and "grass"
• Score: 11.684330305297523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects and regions in LiDAR point clouds. Learning-based models struggle in off-road environments due to the presence of diverse objects with varying colors, textures, and undefined boundaries, which can lead to difficulties in accurately classifying and segmenting objects using traditional geometric-based features. In this paper, we address this problem by harnessing the LiDAR intensity parameter to enhance object segmentation in off-road environments. Our approach was evaluated in the RELLIS-3D data set and yielded promising results as a preliminary analysis with improved mIoU for classes "puddle" and "grass" compared to more complex deep learning-based benchmarks. The methodology was evaluated for compatibility across both Velodyne and Ouster LiDAR systems, assuring its cross-platform applicability. This analysis advocates for the incorporation of calibrated intensity as a supplementary input, aiming to enhance the prediction accuracy of learning based semantic segmentation frameworks. https://github.com/MOONLABIISERB/lidar-intensity-predictor/tree/main

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