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Robust and persistent localisation is essential for ensuring the safeoperation of autonomous vehicles. When operating in large and diverse urbandriving environments, autonomous vehicles are frequently exposed to situationsthat violate the assumptions of algorithms, suffer from the failure of one ormore sensors, or other events that lead to a loss of localisation.  This paper proposes the use of a behavior tree framework that can monitor theperformance of localisation health metrics and triggers intelligent responsessuch as sensor switching and loss recovery. The algorithm presented selects thebest available sensor data at given time and location, and can perform a seriesof actions to react to adverse situations. The behavior tree encapsulates thesystem-level logic to give commands that make up the intelligent behaviors, sothat the localisation "actuators" (data association, optimisation, filters,etc) can perform decoupled actions without needing context. Experimentalresults to validate the algorithms are presented using the University of SydneyCampus dataset which was taken weekly over an 18 month period. A video showingthe online localisation process can be found here: https://youtu.be/353uKqXLV5g

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving