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VI-SLAM for Subterranean Environments

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Field and Service Robotics

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 16))

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Abstract

Among the most challenging of environments in which an autonomous mobile robot might be required to serve is the subterranean environment. The complete lack of ambient light, unavailability of GPS, and geometric ambiguity make subterranean simultaneous localization and mapping (SLAM) exceptionally difficult. While there are many possible solutions to this problem, a visual-inertial framework has the potential to be fielded on a variety of robotic platforms which can operate in the spatially constrained and hazardous environments presented by the subterranean domain. In this work, we present an evaluation of visual-inertial SLAM in the subterranean environment with onboard lighting and show that it can consistently perform quite well, with less than 4% translational drift. However, this performance is dependent on including some modifications that depart from the typical formulation of VI-SLAM, as well as careful tuning of the system’s visual tracking parameters. We discuss the sometimes counter-intuitive effects of these parameters and provide insight into how they affect the system’s overall performance.

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References

  1. Darpa subterranean (subt) challenge. https://www.darpa.mil/program/darpa-subterranean-challenge

  2. Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31, 1147–1163 (2015)

    Article  Google Scholar 

  3. Carlone, L., Karaman, S.: Attention and anticipation in fast visual-inertial navigation. IEEE Trans. Robot. 35(1), 1–20 (2019)

    Article  Google Scholar 

  4. Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992.) http://dblp.uni-trier.de/db/journals/pami/pami14.html#BeslM92

  5. Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint kalman filter for vision-aided inertial navigation. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3565–3572 (2007)

    Google Scholar 

  6. Sibley, G.: A sliding window filter for slam (2006)

    Google Scholar 

  7. Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.T.: Keyframe-based visual–inertial odometry using nonlinear optimization. Int. J. Robot. Res. (IJRR) (2016)

    Google Scholar 

  8. Qin, T., Pan, J., Cao, S., Shen, S.: A general optimization-based framework for local odometry estimation with multiple sensors (2019)

    Google Scholar 

  9. Scherer, S., Dubé, D., Zell, A.: Using depth in visual simultaneous localisation and mapping. In: 2012 IEEE International Conference on Robotics and Automation, pp. 5216–5221 (2012)

    Google Scholar 

  10. Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration for real-time visual–inertial odometry. IEEE Trans. Robot. 33(1), 1–21 (2017). ISSN 1552-3098, 1941-0468. https://doi.org/10.1109/TRO.2016.2597321. https://ieeexplore.ieee.org/document/7557075/

  11. Nobre, F., Heckman, C.R., Sibley, G.T.: Multi-sensor slam with online self-calibration and change detection, pp. 764–774, 03 2017. ISBN 978-3-319-50114-7. https://doi.org/10.1007/978-3-319-50115-4_66

  12. Leutenegger, S., Chli, M., Siegwart, R.Y.: Brisk: binary robust invariant scalable keypoints. In: Proceedings of the 2011 International Conference on Computer Vision, ICCV ’11, pp. 2548–2555. Washington, DC, USA, 2011. IEEE Computer Society. ISBN 978-1-4577-1101-5. https://doi.org/10.1109/ICCV.2011.6126542

  13. Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3400–3407. Shanghai, China, May 2011, IEEE. ISBN 978-1-61284-386-5. https://doi.org/10.1109/ICRA.2011.5979561. http://ieeexplore.ieee.org/document/5979561/

  14. Wang, J., Olson, E.: AprilTag 2: efficient and robust fiducial detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4193–4198. Daejeon, South Korea, October 2016. IEEE. ISBN 978-1-5090-3762-9. https://doi.org/10.1109/IROS.2016.7759617. http://ieeexplore.ieee.org/document/7759617/

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Correspondence to Andrew Kramer .

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Kramer, A., Kasper, M., Heckman, C. (2021). VI-SLAM for Subterranean Environments. In: Ishigami, G., Yoshida, K. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-15-9460-1_12

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