UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised

TL;DR

UdeerLID+ is a semi-supervised framework that combines LiDAR, images, and relative depth data to improve road segmentation accuracy for autonomous driving, validated on KITTI datasets.

Contribution

The paper introduces a novel semi-supervised approach integrating multiple sensor modalities for robust road segmentation.

Findings

Superior performance on KITTI dataset

Effective integration of LiDAR, image, and depth data

Addresses data scarcity in training deep models

Abstract

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.