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

TL;DR

This paper introduces W-RIZZ, a weakly-supervised framework that efficiently estimates relative traversability in outdoor mobile robotics, reducing labeling effort and improving performance over existing methods.

Contribution

It presents a novel weakly-supervised approach with a new labeling strategy and loss function for better traversability estimation in unstructured environments.

Findings

Reduces labeling effort compared to segmentation methods

Achieves effective traversability estimation in outdoor tests

Outperforms some existing self-supervised approaches

Abstract

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.