Learning temporal co-observability relationships for lifelong robotic mapping

Summary

Nicholas Carlevaris-Bianco and Ryan M. Eustice, Learning temporal co-observability relationships for lifelong robotic mapping. In IROS Workshop on Lifelong Learning for Mobile Robotics Applications, Vilamoura, Portugal, October 2012.

Abstract

This paper reports on a method that learns the temporal co-observability relationships for exemplar views of a dynamic environment collected during long-term robotic mapping. These relationships are efficiently captured using a Chow-Liu tree approximation and allow one to predict which exemplars will be observed by the robot given the robot's recent observations. For example, these learned relationships can encode scene dependent changes in lighting due to time of day and weather, without explicitly modeling them. Preliminary experimental results are shown using images from 17 fixed locations collected hourly over the course of 116 days.

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