Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions

TL;DR

This paper presents a novel trajectory-based self-supervised road segmentation method that fuses lidar and camera data, significantly improving performance in winter driving conditions without manual labeling.

Contribution

It introduces a joint lidar-camera trajectory-based learning approach, enhancing robustness and accuracy in challenging winter environments compared to existing methods.

Findings

Outperforms recent standalone lidar and camera methods

Effective in winter countryside and suburb scenes

No manual labels required for training

Abstract

Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging as each sample must be labeled by hand. Trajectory-based self-supervised methods offer a potential solution as they can learn from the traversed route without manual labels. However, existing trajectory-based methods use learning schemes that rely only on the camera or only on the lidar. In this paper, trajectory-based learning is implemented jointly with lidar and camera for increased performance. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset including countryside and suburb driving scenes. The source code is available at https://github.com/eerik98/lidar-camera-road-autolabeling.git