Localisation in large-scale environments

Abstract

This paper describes a localisation framework that combines the accuracy of feature maps with the scalability of topological maps. The map is structured as a graph of nodes where each node defines a local region feature map. This breaks the localisation process into a combination of regional feature tracking and node-to-node context switching. As part of the practical implementation of the localisation system, we introduce a batch data association method that uses the simultaneous observation of multiple features to determine data associations in a manner decoupled from the vehicle pose estimate. We also present an observation-based dead reckoning procedure that estimates vehicle motion in place of odometry and does not require a kinematic vehicle model. Experimental results demonstrate that this approach is capable of localising in large-scale outdoor environments. We perform tests in an inner city park and a suburban street using a scanning range laser as the sole information source. The diverse nature of these two environments indicates that these techniques have broad application.