Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss

TL;DR

This paper introduces a deep learning approach for outdoor robot traversability estimation from RGB images, incorporating domain adaptation and safety-focused loss to improve generalization and safety in navigation.

Contribution

It presents a novel domain adaptation method with safety-preserving loss for improved traversability estimation from RGB data in outdoor environments.

Findings

Effective in identifying traversable areas

Generalizes well to unseen locations

Prioritizes safety by penalizing risky errors

Abstract

Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontinuities in detected distances due to perspective. In this paper, we present an approach based on deep learning to estimate and anticipate the traversing score of different routes in the field of view of an on-board RGB camera. The backbone of the proposed model is based on a state-of-the-art deep segmentation model, which is fine-tuned on the task of predicting route traversability. We then enhance the model's capabilities by a) addressing domain shifts through gradient-reversal unsupervised adaptation, and b) accounting for the specific safety requirements of a mobile robot, by encouraging the model to err on the safe side, i.e., penalizing errors that would cause collisions with obstacles more than those that would cause the robot to stop in advance. Experimental results show that our approach is able to satisfactorily identify traversable areas and to generalize to unseen locations.