Abstract
A tracking system that will be used for augmented reality applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay, occurring because of the delay in estimating the motion of the user and displaying images based on this estimate). This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS–IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context running at modest frame rates due to the design of the fusion algorithm.
Similar content being viewed by others
Notes
References
Agrawal M, Konolige K, Bolles R (2007) Localization and mapping for autonomous navigation in outdoor terrains: a stereo vision approach. In: IEEE workshop on applications of computer vision, pp 7–12
Almagbile A, Wang J, Ding W (2010) Evaluating the performances of adaptive kalman filter methods in GPS/INS integration. J Glob Position Syst 9(1):33–40
Armesto L, Tornero J, Vincze M (2007) Fast ego-motion estimation with multi-rate fusion of inertial and vision. Int J Robot Res 26:577–589
Azuma R (1997) A survey of augmented reality. Presence Teleoperators Virtual Environ 6:355–385
Bleser G (2009) Towards visual-inertial slam for mobile augmented reality. Ph.D. thesis, Fachbereich Informatik der Technischen Universitt Kaiserslautern
Bostanci E, Clark A, Kanwal N (2012) Vision-based user tracking for outdoor augmented reality. In: 2012 IEEE symposium on computers and communications (ISCC), pp 566–568
Bostanci E, Kanwal N, Ehsan S, Clark AF (2013) User tracking methods for augmented reality. Int J Comput Theory Eng 5(1):93–98
Broll W, Lindt I, Herbst I, Ohlenburg J, Braun A, Wetzel R (2008) Toward next-gen mobile are games. IEEE computer graphics and applications, pp 40–48
Chen J, Pinz A (2004) Structure and motion by fusion of inertial and vision-based tracking. Austrian Assoc Pattern Recognit 179:75-62
Chroust SG, Vincze M (2004) Fusion of vision and inertial data for motion and structure estimation. J Robotic Syst 21(2):73–83
Civera J, Davison A, Montiel JMM (2008) Interacting multiple model monocular slam. In: International conference on robotics and automation, pp 3704–3709
Groves P (2008) Principles of GNSS, inertial, and multi-sensor integrated navigation systems. GNSS technology and applications series, Artech House
Hong SK (2003) Fuzzy logic based closed-loop strapdown attitude system for unmanned aerial vehicle (uav). Sens Actuators A Phys 107(2):109–118
Kanatani K (2004) Uncertainty modeling and model selection for geometric inference. IEEE Trans Pattern Anal Mach Intell 26(10):1307–1319
Kaplan E, Hegarty C (2005) Understanding GPS: principles and applications. Artech house mobile communications series, Artech house
Kleeman L (2013) Understanding and applying kalman filtering. http://www.ecse.monash.edu.au/centres/irrc/LKPubs/Kalman.PDF. Accessed Sep 2013
Konolige K, Agrawal M, Bolles R, Cowan C, Fischler M, Gerkey B (2008) Outdoor mapping and navigation using stereo vision. Exp Robot 39:179–190
Kramer J, Kandel A (2012) On accurate localization and uncertain sensors. Int J Intell Syst 27(5):429–456
Lang P, Kusej A, Pinz A, Brasseur G (2002) Inertial tracking for mobile augmented reality. In: IEEE instruments and measurement technology conference
Madgwick S, Vaidyanathan R, Harrison A (2010) An efficient orientation filter for inertial measurement units (IMUs) and magnetic angular rate and gravity (MARG) sensor arrays. Technical report, Department of Mechanical Engineering
Ojeda L, Borenstein J (2002) Flexnav: fuzzy logic expert rule-based position estimation for mobile robots on rugged terrain. In: IEEE international conference on robotics and automation, 2002. Proceedings. ICRA ’02, vol 1, pp 317–322
Oskiper T, Samarasekera S, Kumar R (2012) Multi-sensor navigation algorithm using monocular camera, imu and gps for large scale augmented reality. In: 2012 IEEE international symposium on mixed and augmented reality (ISMAR), pp 71–80
Read P, Meyer M (2000) Restoration of motion picture film. Elsevier Science, Butterworth-Heinemann series in conservation and museology
Reid I (2012) Estimation II. http://www.robots.ox.ac.uk/~ian/Teaching/Estimation/LectureNotes2.pdf. Accessed Sep 2013
Ribo M, Lang P, Ganster H, Brandner M, Stock C, Pinz A (2002) Hybrid tracking for outdoor augmented reality applications. IEEE computer graphics and applications, pp 54–63
Ross T (2009) Fuzzy logic with engineering applications. Wiley, New York
Schindler K, Suter D (2006) Two-view multibody structure-and-motion with outliers through model selection. IEEE Trans Pattern Anal Mach Intell 28(6):983–995
Schleicher D, Bergasa LM, Ocana M, Barea R, Lopez M (2009) Real-time hierarchical outdoor slam based on stereovision and gps fusion. IEEE Trans Intell Transp Syst 10:440–452
Seo J, Lee JG, Park C (2005) Leverarm compensation for integrated navigation system of land vehicles. In: Proceedings of 2005 IEEE conference on control applications, 2005. CCA 2005, pp 523–528
Thrun S, Burgard W, Fox D (2006) Probabilistic robotics. MIT Press, New York
Tornqvist D, Schon TB, Karlsson R, Gustafsson F (2009) Particle filter slam with high dimensional vehicle model. J Intell Robot Syst 55:249–266
Torr P (2002) Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. Int J Comput Vision 50(1):35–61
Tseng C, Chang C, Jwo D (2011) Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion. Sensors 11:2090–2111
You S, Neumann U, Azuma R (1999) Orientation tracking for outdoor augmented reality registration. IEEE virtual reality, pp 36–42
Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Bostanci, E., Bostanci, B., Kanwal, N. et al. Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models. Soft Comput 22, 2619–2632 (2018). https://doi.org/10.1007/s00500-017-2516-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2516-8