Occupancy Map Building through Bayesian Exploration
This work addresses the challenge of efficient and safe occupancy map building for autonomous robots, representing an incremental improvement over existing exploration techniques.
The paper tackles the problem of safe autonomous exploration and map building by proposing a novel holistic approach based on constrained Bayesian optimization, which finds optimal continuous paths that satisfy motion and safety constraints, and demonstrates robust performance in simulations and real robot experiments, performing as well as or better than other leading methods.
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.