Abstract
Autonomous path-following systems based on the Teach and Repeat paradigm allow robots to traverse extensive networks of manually driven paths using on-board sensors. These methods are well suited for applications that involve repeated traversals of constrained paths such as factory floors, orchards, and mines. In order for path-following systems to be viable for these applications they must be able to navigate large distances over long time periods, a challenging task for vision-based systems that are susceptible to appearance change. This paper details Visual Teach and Repeat 2.0, a vision-based path-following system capable of safe, long-term navigation over large-scale networks of connected paths in unstructured, outdoor environments. These tasks are achieved through the use of a suite of novel, multi-experience, vision-based navigation algorithms. We have validated our system experimentally through an eleven-day field test in an untended gravel pit in Sudbury, Canada, where we incrementally built and autonomously traversed a 5 Km network of paths. Over the span of the field test, the robot logged over 140 Km of autonomous driving with an autonomy rate of 99.6%, despite experiencing significant appearance change due to lighting and weather, including driving at night using headlights.
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Notes
- 1.
We use SURF features triangulated from greyscale and color-constant stereo measurements in our implementation, but the overall system is generic to any point-based, sparse visual feature.
References
Anderson S., Barfoot, T.D.: Full steam ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on se(3). In: IROS (2015)
Berczi, L.-P., Barfoot, T.D.: It’s like déjà vu all over again: Learning place-dependent terrain assessment for visual teach and repeat. In: IROS (2016)
Berczi, L.-P., Posner, I., Barfoot, T.D.: Learning to assess terrain from human demonstration using an introspective gaussian-process classifier. In: ICRA (2015)
Chen, Z., Birchfield, S.T.: Qualitative vision-based path following. IEEE Trans. Robot. 25(3), 749–754 (2009)
Churchill, W.S., Newman, P.: Experience-based navigation for long-term localisation. IJRR 32(14), 1645–1661 (2013)
Clement, L., Kelly, J., Barfoot, T.D.: Robust monocular visual teach and repeat aided by local ground planarity and color-constant imagery. JFR 34(1), 74–97 (2017)
Furgale, P., Barfoot, T.D.: Visual teach and repeat for long-range rover autonomy. JFR 27(5), 534–560 (2010)
Goldberg, S.B., Maimone, M.W., Matthies, L.: Stereo vision and rover navigation software for planetary exploration. In IEEE Aerospace Conference Proceedings (2002)
Jackel, L.D., Krotkov, E., Perschbacher, M., Pippine, J., Sullivan, C.: The DARPA LAGR program: Goals, challenges, methodology, and phase I results. JFR 23(11–12), 945–973 (2006)
Krajnk, T., Faigl, J., Vonsek, V., Konar, K., Kulich, M., Peuil, L.: Simple yet stable bearing-only navigation. JFR 27(5), 511–533 (2010)
Krüsi, P., Bücheler, B., Pomerleau, F., Schwesinger, U., Siegwart, R., Furgale P.: Lighting-Invariant Adaptive Route Following Using ICP. JFR (2014)
Linegar, C., Churchill, W., Newman, P.: Work Smart. Recalling relevant experiences for vast-scale but time-constrained localisation. In: ICRA, Not Hard (2015)
MacTavish, K., Barfoot, T.D.: Towards hierarchical place recognition for long-term autonomy. In: ICRA Workshop (2014)
MacTavish, K., Paton, M., Barfoot, T.D.: Visual triage: a bag-of-words experience selector for long-term visual route following. In: ICRA (2017)
McManus, C., Furgale, P., Stenning, B., Barfoot, T.D.: Visual teach and repeat using appearance-based lidar. In: ICRA (2012)
Mhlfellner, P., Brki, M., Bosse, M., Derendarz, W., Philippsen, R., Furgale, P.: Summary maps for lifelong visual localization. JFR (2015)
Ostafew, C., Schoellig, A.P., Barfoot, T.D., Collier, J.: Learning-based nonlinear model predictive control to improve vision-based mobile robot path tracking. JFR 33(1), 133–152 (2016)
Paton, M., MacTavish, K., Ostafew, C., Pomerleau, F., Barfoot, T.D.: Expanding the limits of vision-based localization for long-term route following autonomy. JFR 34(1), 98–122 (2017)
Paton, M., MacTavish, K., Warren, M., Barfoot, T.D.: Bridging the appearance gap: Multi-experience localization for long-term visual teach & repeat. In: IROS (2016)
Sibley, G., Mei, C., Reid, I., Newman, P.: Adaptive relative bundle adjustment. In: RSS (2009)
van Es, K., Barfoot, T.D.: Being in two places at once: Smooth visual path following on globally inconsistent pose graphs. In: CRV (2015)
Acknowledgements
This work was supported financially and in-kind by Clearpath Robotics and the Natural Sciences and Engineering Research Council (NSERC) through the NSERC Canadian Field Robotics Network (NCFRN). The authors would like to also extended their deepest thanks to Ethier Sand and Gravel for allowing us to conduct our field test at their site.
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Paton, M., MacTavish, K., Berczi, LP., van Es, S.K., Barfoot, T.D. (2018). I Can See for Miles and Miles: An Extended Field Test of Visual Teach and Repeat 2.0. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_27
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DOI: https://doi.org/10.1007/978-3-319-67361-5_27
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