Abstract
This paper is concerned with real-time monocular visual inertial simultaneous localization and mapping (VI-SLAM). In particular a tightly coupled nonlinear-optimization based solution that can match the global optimal result in real time is proposed. The methodology is motivated by the requirement to produce a scale-correct visual map, in an optimization framework that is able to incorporate relocalization and loop closure constraints. Special attention is paid to achieve robustness to many real world difficulties, including degenerate motions and unobservablity. A variety of helpful techniques are used, including: a relative manifold representation, a minimal-state inverse depth parameterization, and robust non-metric initialization and tracking. Importantly, to enable real-time operation and robustness, a novel numerical dog leg solver [16] is presented that employs multithreaded, asynchronous, adaptive conditioning. In this approach, the conditioning edges of the SLAM graph are adaptively identified and solved for both synchronously and asynchronously. In this way some threads focus on a small number of temporally immediate parameters and hence constitute a natural “front-end”; other threads adaptively focus on larger portions of the SLAM problem, and hence are able to capture functional constraints that are only observable over long periods of time—an ability which is useful for self-calibration, during degenerate motions, or when bias and gravity are poorly observed. Experiments with real and simulated data for both indoor and outdoor robots demonstrate that asynchronous adaptive conditioning is able to closely track the full-SLAM maximum likelihood solution in real-time, even during challenging non-observable and degenerate cases.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Engels, C., Stewenius, H., Nister, D.: Bundle adjustment rules. In: Photogrammetric Computer Vision (2006)
Gelb, A.: Applied Optimal Estimation. MIT Press, Cambridge (1974)
Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I.: Camera-imu-based localization: observability analysis and consistency improvement. Int. J. Robot. Res. 33, 182–201 (2013)
Jones, E., Vedaldi, A., Soatto, S.: Inertial structure from motion with autocalibration. In: ICCV Workshop on Dynamical Vision (2007)
Kelly, J., Sukhatme, G.S.: Visual-inertial sensor fusion: localization, mapping and sensor-to-sensor self-calibration. Int. J. Robot. Res. (2010)
Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: International Symposium on Mixed and Augmented Reality (2007)
Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., Siegwart, R.: Keyframe-based visual-inertial slam using nonlinear optimization. In: Robotics Science and Systems (2013)
Li, M., Kim, B., Mourikis. A.I.: Real-time cellphone localization using inertial sensing and a rolling-shutter camera. In: Proceedings of the IEEE International Conference on Robotics and Automation (2013)
Li, M., Mourikis, A.I.: High-precision, consistent ekf-based visual-inertial odometry. Int. J. Robot. Res. 33, 690–711 (2014)
Maybeck, P.S.: Stochastic models, estimation, and control. In: Mathematics in Science and Engineering, vol. 141. Academic Press Inc, Boston (1979)
Mei, C., Sibley, G., Cummins, M., Newman, P., Reid, I.: RSLAM: a system for large-scale mapping in constant-time using stereo. Int. J. Comput. Vis. 1–17 (2010)
Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyse, F., Sayd, P.: Real time localization and 3d reconstruction. In: Proceedings of Computer Vision and Pattern Recognition, New York, June 2006
Mourikis, A., Roumeliotis, S.: A multi-state constraint kalman filter for vision-aided inertial navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3565–3572, Rome, April 2007
Nerurkar, E.D., Wu, K.J., Roumeliotis, S.I.: C-klam: constrained keyframe localization and mapping for long-term navigation. In: IEEE International Conference on Robotics and Automation Workshop on Long-term Autonomy (2013)
Pietzsch, T.: Efficient feature parameterisation for visual SLAM using inverse depth bundles. In: British Machine Vision Conference (2008)
Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)
Scaramuzza, D., Forster, C., Pizzoli, M.: Svo: fast semi-direct monocular visual odometry. In: IEEE Conference on Robotics and Automation (2014)
Sibley, G., Matthies, L., Sukhatme, G.: Sliding window filter with applications to planetary landing. J. Field Robot. 27(5), 587–608 (2010)
Sibley, G., Mei, C., Ried, I., Newman, P.: Adaptive relative bundle adjustment. In: Robotics Science and Systems (2009)
Sibley, G.: Sliding window filters for SLAM. Technical Report, University of Southern California, Center for Robotics and Embedded Systems, CRES-06-004 (2006)
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: ICCV ’99: Proceedings of the International Workshop on Vision Algorithms, pp. 298–372. Springer-Verlag, London (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Keivan, N., Patron-Perez, A., Sibley, G. (2016). Asynchronous Adaptive Conditioning for Visual-Inertial SLAM. In: Hsieh, M., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-319-23778-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-23778-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23777-0
Online ISBN: 978-3-319-23778-7
eBook Packages: EngineeringEngineering (R0)