Experience-based Navigation

This work addresses the difficult problem of navigation in changing, dynamic environments. Assuming the world is static in appearance results in brittle mapping and localisation systems. Change comes from many sources (dynamic objects, time of day, weather, seasons) and over different time scales (minutes, hours, days, months). In this work we look to account for these variations by capturing distinct experiences of the world. Using the concert of experiences we can produce richer, more informative maps and better localisation performance.

•  C. Linegar, W. Churchill, and P. Newman, “Work Smart, Not Hard: Recalling Relevant Experiences for Vast-Scale but Time-Constrained Localisation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
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•  W. Churchill and P. Newman, “Experience-based Navigation for Long-term Localisation,” The International Journal of Robotics Research (IJRR), 2013.
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•  W. Churchill and P. Newman, “Continually Improving Large Scale Long Term Visual Navigation of a Vehicle in Dynamic Urban Environments,” in Proc. IEEE Intelligent Transportation Systems Conference (ITSC), Anchorage, USA, 2012.
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•  W. Churchill and P. Newman, “Practice Makes Perfect? Managing and Leveraging Visual Experiences for Lifelong Navigation,” in Proc. IEEE International Conference on Robotics and Automation (ICRA), Minnesota, USA, 2012.
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