Off-Road LiDAR Intensity Based Semantic Segmentation
Kasi Viswanath Peng Jiang Sujit PB Srikanth Saripalli
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