Localisation and Mapping for an Autonomous Lawn Mower : Implementation of localisation and mapping features for an autonomous lawn mower using heterogeneous sensors

Abstract: Autonomous lawn mowers have been available to consumers for more than 20 years. During this period, advancements in embedded device computations and sensor performance have led to improvements in the reliability of these robots. Despite recent improvements, the opportunity for further innovation of such systems remains significant. Currently, many autonomous robots rely on electric wires installed underground to delimit the boundaries of the lawn. Such a configuration is simple, but more effective autonomous solutions are available. This thesis focuses on the analysis and related implementation of both localisation and mapping features for autonomous lawn mowers. Heterogeneous sensors and their different configurations are investigated and an Adaptive Extended Kalman Filter is proposed to fuse their measurements. This technique improves the pose estimation of the autonomous lawn mower, which is then exploited by the mapping module. Based on Bayesian’s inference, the mapping module updates the knowledge of the map based on direct interactions with the environment. The final results highlight the importance of precise localisation as the bottleneck for the development of new features. The improved pose estimation enables the employment of a virtual boundary, but it is not accurate enough to precisely map the presence of objects in the environment. Advanced features which could be developed from the proposed configuration are related to deterministic coverage algorithms and the interaction with lawn objects.