A robust and modular multi-sensor fusion approach applied to MAV navigation

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2013, Pages 3923-3929

  Lynen, Simon ^a   Achtelik, Markus W ^a   Weiss, Stephan ^b   Chli, Margarita ^a   Siegwart, Roland ^a  

^a ETH ZURICH   (Switzerland)

^b Nasa JPL ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABSOLUTE MEASUREMENTS; GENERIC FRAMEWORKS; MICRO AERIAL VEHICLE; MULTI-SENSOR FUSION; MULTIPLE SENSORS; OUTDOOR NAVIGATION; ROBOT NAVIGATION; SENSOR INFORMATIONS;

CALIBRATION; EXTENDED KALMAN FILTERS; GLOBAL POSITIONING SYSTEM; INTELLIGENT ROBOTS; LINEARIZATION; MICRO AIR VEHICLE (MAV); NAVIGATION;

SENSORS;

EID: 84893739860     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2013.6696917     Document Type: Conference Paper

References (17)

1. G. Chowdhary, E. Johnson, D. Magree, D. Wu, and A. Shein. GPSDenied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft. Journal of Field Robotics (JFR), 2013.
2. M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981.
3. S. Grzonka, G. Grisetti, and W. Burgard. Towards a navigation system for autonomous indoor flying. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), Kobe, Japan, 2009. IEEE.
4. R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, second edition, 2004.
5. J. A. Hesch, D. G. Kottas, S. L. Bowman, and S. I. Roumeliotis. Towards consistent vision-Aided inertial navigation. In Algorithmic Foundations of Robotics X. Springer, 2013.
6. V. Indelman, S. Williams, M. Kaess, and F. Dellaert. Factor graph based incremental smoothing in inertial navigation systems. In Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012.
7. G. Klein. Visual Tracking for Augmented Reality. PhD thesis, University of Cambridge, 2006.
8. S. Leutenegger and R. Y. Siegwart. A low-cost and fail-safe inertial navigation system for airplanes. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), Karlsruhe, Germany, 2012. IEEE.
9. M. Li and A. I. Mourikis. High-precision, consistent EKF-based visual-inertial odometry. The International Journal of Robotics Research, (IJRR), 2013.
10. F. M. Mirzaei and S. I. Roumeliotis. A kalman filter-based algorithm for imu-camera calibration: Observability analysis and performance evaluation. Robotics, IEEE Transactions on, 24(5):1143-1156, 2008.
11. A. Mourikis and S. Roumeliotis. A multi-state constraint Kalman filter for vision-Aided inertial navigation. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2007.
12. A. I. Mourikis, S. I. Roumeliotis, and J. W. Burdick. SC-KF mobile robot localization: a stochastic cloning Kalman filter for processing relative-state measurements. Robotics, IEEE Transactions on, 2007.
13. S. Shen, N. Michael, and V. Kumar. Autonomous multi-floor indoor navigation with a computationally constrained mav. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA). IEEE, 2011.
14. S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar. Vision-based state estimation and trajectory control towards aggressive flight with a quadrotor. In Robotics Science and Systems, 2013.
15. S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar. Vision-based state estimation for autonomous rotorcraft MAVs in complex environments. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2013.
16. S. Weiss. Vision based navigation for micro helicopters. PhD thesis, ETH Zurich, 2012.
17. S. Weiss, M. W. Achtelik, S. Lynen, M. Chli, and R. Siegwart. Realtime onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.