  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
