W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics

• Published in: IEEE robotics and automation letters 2024-06, Vol.9 (6), p.5623-5630
• Main Authors: Schreiber, Andre, Sivakumar, Arun N., Du, Peter, Gasparino, Mateus V., Chowdhary, Girish, Driggs-Campbell, Katherine
• Format: Article
• Language: English
• Subjects: Annotations; deep learning for visual perception; Estimation; Field robots; Hazardous areas; Labeling; Labels; Methods; Navigation; Performance enhancement; Point pairs; Robotics; Robots; Semantic segmentation; Semantics; vision-based navigation
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2024.3396095

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation-which predicts where in the environment a robot can travel-is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments.