Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency

TL;DR

This paper introduces a lightweight, self-supervised method for adapting visual navigation agents to noisy environments, improving their localization, mapping, and navigation performance without requiring ground-truth data.

Contribution

It presents a novel self-supervised domain adaptation technique that maximizes global map consistency, enabling quick transfer of agents to noisy environments without explicit supervision.

Findings

Improved localization and mapping accuracy in noisy environments

Enhanced downstream navigation performance

Effective test-time adaptation demonstrated

Abstract

We propose a light-weight, self-supervised adaptation for a visual navigation agent to generalize to unseen environment. Given an embodied agent trained in a noiseless environment, our objective is to transfer the agent to a noisy environment where actuation and odometry sensor noise is present. Our method encourages the agent to maximize the consistency between the global maps generated at different time steps in a round-trip trajectory. The proposed task is completely self-supervised, not requiring any supervision from ground-truth pose data or explicit noise model. In addition, optimization of the task objective is extremely light-weight, as training terminates within a few minutes on a commodity GPU. Our experiments show that the proposed task helps the agent to successfully transfer to new, noisy environments. The transferred agent exhibits improved localization and mapping accuracy, further leading to enhanced performance in downstream visual navigation tasks. Moreover, we demonstrate test-time adaptation with our self-supervised task to show its potential applicability in real-world deployment.