Abstract
This paper presents a novel stereo image-based image aided inertial navigation algorithm for reducing position and orientation drifts during GNSS outages or in a poor GNSS environment. Usually, the image aided navigation based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from all overlapping image frames towards accuracy improvement and drift reduction. The measurement equation system in this multi-frame visual odometry algorithm (MFVO) is derived from Simultaneous Localization and Mapping (SLAM) measurement equation system where the landmark position parameters in SLAM are algebraically eliminated by time-differencing the measurement at two consecutive epochs. However the resulted time-differenced measurements are time-correlated. Through a sequential de-correlation the Kalman filter measurement update can be performed sequentially and optimally. Monte Carlo simulations show that the MFVO and SLAM pose estimates are similar. Compared with SLAM, the proposed method uses less computation resources especially when the number of features in view is large. The results from a real dataset are also presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alcantarilla PF, Jos´e JY, Javier A, Bergasa LM (2012) On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments. In: IEEE International conference on robotics and automation RiverCentre, Saint Paul, Minnesota, USA, 14–18 May 2012
Bierman GJ (1977) Factorization methods for discrete sequential estimation. ISBN 0-486-44981-5, p 47
Durrant-Whyte H, Bailey T (2006) Simultaneous localisation and mapping (SLAM): part II State of the art. Robot Automat Mag 13(3):108–117
Gopaul NS, Wang J, Hu B (2014) Discrete EKF with pairwise time-correlated measurement noise for image-aided inertial integrated navigation. In: Joint ISPRS/IGU 2014 conference, 6–8 Oct 2014, Toronto, Canada
Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice using MATLAB, 2nd edn, p 221
Hartley R, Zisserrnan A (2000): Multiple view geometry in computer vision. Cambridge
Indelman V, Melim A, Dellaert F (2013) Incremental light bundle adjustment for robotics navigation. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), Tokyo, Japan, 3–7 Nov 2013
Jeong Y, Nister D, Steedly D, Szeliski R, Kweon I (2012) Pushing the envelope of modern methods for bundle adjustment. IEEE Trans Pattern Anal Mach Intell 34(8):1605–1617
Julier SJ (2001) A sparse weight Kalman filter approach to simultaneous localisation and map building. In: Proceedings of the 2001 IEEE/IRJ, international conference on intelligent robots and systems Maui, Hawaii, USA, 29 Oct–03 Nov 2001
Konolige K, Agrawal M, Sol’a J (2007): Large scale visual odometry for rough terrain. In: International symposium on research in robotics, pp 201–212, Nov 2007
Lategahn H, Geiger A, Kitt B (2011) Visual SLAM for autonomous ground vehicles. In: IEEE International conference on robotics and automation shanghai international conference center, Shanghai, China, 9–13 May 2011
Guivant JE, Nebot, EM (2001) Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans Robot Autom 17(3)
Qian K, Gopaul N, Wang J-G, Baoxin Hu (2012) Low cost multisensor kinematic positioning and navigation system with Linux/RTAI. J Sens Actuator Netw 1(3):166–182
Sazdovski V, Peter M, Silson G (2011) Inertial navigation aided by vision-based simultaneous localization and mapping. IEEE Sens J 11(8):1646–1656
Scaramuzza D, Fraundorfer F (2011) Visual odometry part i: the first 30 years and fundamentals. IEEE Robot Autom Mag 18(4):80–92
Tardif JP, George M, Laverne M, Kelly A, Stentz A (2010) A new approach to vision-aided inertial navigation. In: International conference on intelligent robots and systems
Triggs N, McLauchlan P, Hartley R, Fitzgibbon A (2000) Bundle adjustment—a modern synthesis. In: Triggs B, Zisserman A, Szeliski R (eds) Proceedings on international workshop vision algorithms: theory and practice, pp 298–372
Acknowledgments
The authors would like to acknowledge the financial support through research grants provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gopaul, N.S., Wang, J.G., Hu, B. (2015). Multi-frame Visual Odometry in Image-Aided Inertial Navigation System. In: Sun, J., Liu, J., Fan, S., Lu, X. (eds) China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III. Lecture Notes in Electrical Engineering, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46632-2_57
Download citation
DOI: https://doi.org/10.1007/978-3-662-46632-2_57
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46631-5
Online ISBN: 978-3-662-46632-2
eBook Packages: EngineeringEngineering (R0)