Occupancy Map Building through Bayesian Exploration

TL;DR

This paper introduces a Bayesian optimisation-based method for safe, efficient autonomous exploration and occupancy map building by planning optimal continuous paths that satisfy safety constraints, reducing costly evaluations.

Contribution

It presents a novel holistic approach that plans continuous paths using constrained Bayesian optimisation, outperforming existing methods in robustness and efficiency.

Findings

Effective in simulation and real robot experiments

Outperforms or matches leading exploration techniques

Provides robust, consistent performance across tests

Abstract

We propose a novel holistic approach for safe autonomous exploration and map building based on constrained Bayesian optimisation. This method finds optimal continuous paths instead of discrete sensing locations that inherently satisfy motion and safety constraints. Evaluating both the objective and constraints functions requires forward simulation of expected observations. As such evaluations are costly, the Bayesian optimiser proposes only paths which are likely to yield optimal results and satisfy the constraints with high confidence. By balancing the reward and risk associated with each path, the optimiser minimises the number of expensive function evaluations. We demonstrate the effectiveness of our approach in a series of experiments both in simulation and with a real ground robot and provide comparisons to other exploration techniques. Evidently, each method has its specific favourable conditions, where it outperforms all other techniques. Yet, by reasoning on the usefulness of the entire path instead of its end point, our method provides a robust and consistent performance through all tests and performs better than or as good as the other leading methods.