Abstract
Colour-constant images have been shown to improve visual navigation taking place over extended periods of time. These images use a colour space that aims to be invariant to lighting conditions—a quality that makes them very attractive for place recognition, which tries to identify temporally distant image matches. Place recognition after extended periods of time is especially useful for SLAM algorithms, since it bounds growing odometry errors. We present results from the FAB-MAP 2.0 place recognition algorithm, using colour-constant images for the first time, tested with a robot driving a 1 km loop 11 times over the course of several days. Computation can be improved by grouping short sequences of images and describing them with a single descriptor. Colour-constant images are shown to improve performance without a significant impact on computation, and the grouping strategy greatly speeds up computation while improving some performance measures. These two simple additions contribute robustness and speed, without modifying FAB-MAP 2.0.
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Acknowledgments
We would like to extend our deepest thanks to the Natural Sciences and Engineering Research Council (NSERC) through the NSERC Canadian Field Robotics Network (NCFRN), the Canada Foundation for Innovation, the Canadian Space Agency, and MDA Space Missions for providing us with the financial and in-kind support necessary to conduct this research.
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MacTavish, K., Paton, M., Barfoot, T.D. (2016). Beyond a Shadow of a Doubt: Place Recognition with Colour-Constant Images. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_13
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DOI: https://doi.org/10.1007/978-3-319-27702-8_13
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