UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised
This addresses road segmentation for autonomous driving systems, but it is incremental as it builds on existing multi-modal integration approaches.
The paper tackles road segmentation for autonomous driving by integrating LiDAR, images, and relative depth maps, achieving superior performance on KITTI datasets.
Road segmentation is a critical task for autonomous driving systems, requiring accurate and robust methods to classify road surfaces from various environmental data. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps derived from images. The integration of multiple data sources in road segmentation presents both opportunities and challenges. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets that are necessary for training robust deep learning models. To address this, we have developed the [UdeerLID+] framework under a semi-supervised learning paradigm. Experiments results on KITTI datasets validate the superior performance.