Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

TL;DR

This paper introduces a Bayesian optimisation approach that incorporates localisation uncertainty using Gaussian processes, enabling safer navigation for robots in uncertain outdoor terrains.

Contribution

It presents a novel method integrating localisation uncertainty into Bayesian optimisation with Gaussian processes, improving safe navigation in rough terrains.

Findings

Enhanced safety in navigation under localisation uncertainty

Successful real-robot experiments on rough terrain

Outperforms standard Bayesian optimisation methods

Abstract

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.