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Lighting-Invariant Visual Odometry using Lidar Intensity Imagery and Pose Interpolation

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Field and Service Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 92))

Abstract

Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes in outdoor environments in comparison to traditional passive stereo camera imagery. Moreover, for visual navigation methods originally developed using stereo vision, such as visual odometry (VO) and visual teach and repeat (VT&R), scanning lidar can serve as a direct replacement for the passive sensor. This results in systems that retain the efficiency of the sparse, appearance-based techniques while overcoming the dependence on adequate/consistent lighting conditions required by traditional cameras. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric VO estimates, even over short distances (e.g., 5–10 m). This paper revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a novel pose interpolation scheme that explicitly accounts for the exact acquisition time of each feature measurement. In this paper, we present the promising preliminary results of our new method using data generated from a lidar simulator and experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.

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Acknowledgments

We would like to extend our deepest thanks to the staff of the Ethier Sand and Gravel in Sudbury, Ontario, Canada for allowing us to conduct our field tests on their grounds. We also wish to thank Dr. James O’Neill from Autonosys for his help in preparing the lidar sensor for our field tests. In addition, we would also like to acknowledge (from the Autonomous Space Robotics Laboratory) Colin McManus for being instrumental in gathering the data used in this paper, Andrew Lambert for his help in preparing the GPS payload, Paul Furgale and Chi Hay Tong for their work on the GPU SURF algorithm, Goran Basic for designing and assembling the Autonosys payload mount, and Keith Leung for providing onsite photography for the field tests. Lastly, we also wish to thank the Natural Sciences and Engineering Research Council of Canada and the Canada Foundation for Innovation, Defence R&D Canada at Suffield (particularly Jack Collier), the Canadian Space Agency, and MDA Space Missions (particularly Cameron Ower, Raja Mukherji, and Joseph Bakambu) for providing us with the financial and in-kind support necessary to conduct this research.

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Authors and Affiliations

  1. University of Toronto Institute for Aerospace Studies, Toronto, Canada

    Hang Dong & Timothy D. Barfoot

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  1. Hang Dong
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  2. Timothy D. Barfoot
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Correspondence to Hang Dong .

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Editors and Affiliations

  1. Department of Aerospace Engineering, Tohoku University, Sendai, Japan

    Kazuya Yoshida

  2. Tohoku University Graduate School of Information Sciences, Sendai, Japan

    Satoshi Tadokoro

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Dong, H., Barfoot, T.D. (2014). Lighting-Invariant Visual Odometry using Lidar Intensity Imagery and Pose Interpolation. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_22

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  • DOI: https://doi.org/10.1007/978-3-642-40686-7_22

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  • Print ISBN: 978-3-642-40685-0

  • Online ISBN: 978-3-642-40686-7

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