Know your limits: Embedding localiser performance models in teach and repeat maps

This paper is about building maps which not only contain the traditional information useful for localising — such as point features — but also embeds a spatial model of expected localiser performance. This often overlooked second-order information provides vital context when it comes to map use and planning. Our motivation here is to improve the performance of the popular Teach and Repeat paradigm [1] which has been shown to enable truly large-scale field operation. When using the taught route for localisation, it is often assumed the robot is following exactly, or is sufficiently close to, the original path, enabling successful localisation. However, what happens if it is not possible, or not desirable to exactly follow the mapped path? How far off the beaten track can the robot travel before it gets lost? We present an approach for assessing this localisation area around a taught route, which we refer to as the localisation envelope. Using a combination of physical sampling and a Gaussian Process model, we are able to accurately predict the localisation performance at unseen points.