Multi-sensor Geo-localisation for Urban Autonomous Driving

Abstract

Robust and persistent localisation is essential for ensuring the safe operation of autonomous vehicles. When operating in vast and diverse urban driving environments, autonomous vehicles are frequently exposed to operating situations that violate the assumptions of algorithms or lead to a loss of localisation. To guarantee driving safety, localisation systems must achieve a high level of accuracy all of the time and anywhere, without the need for human intervention.

To satisfy these requirements, we propose a novel localisation framework that can coordinate a multiple sensor switching strategy that ensures sensors and feature types are used in suitable environments. Three sensor modalities are built for global localisation observation: GPS, lidar landmarks, and visual features to provide the redundancy and continuous availability of global localisation updates. We demonstrate the localisation performance of the proposed framework in the University of Sydney Campus dataset acquired over an 18 months period.

Accurate localisation for many global sensors relies on a highly consistent long-term map. We developed methodologies to make maps for lidar landmarks and visual features that localisation can repeatedly use during an 18 months period. To enable multi-sensor transition, we developed methods to register maps of different sensors and feature types in the same geographic coordinate system. Map global drift and inter-sensor map biases are also minimized.

Localisation systems are seldom evaluated for their robustness, and localisation ground truth such as RTK is hard to obtain in many urban environments. We propose novel metrics to effectively quantify localisation robustness without requiring accurate ground truth. We use these metrics to conduct a comprehensive analysis of the application of these metrics against single and multi-modal localisation strategies developed in this thesis.