Abstract
Accurate state estimation for a mobile robot often requires the fusion of data from multiple sensors. Software that performs sensor fusion should therefore support the inclusion of a wide array of heterogeneous sensors. This paper presents a software package, robot_localization, for the robot operating system (ROS). The package currently contains an implementation of an extended Kalman filter (EKF). It can support an unlimited number of inputs from multiple sensor types, and allows users to customize which sensor data fields are fused with the current state estimate. In this work, we motivate our design decisions, discuss implementation details, and provide results from real-world tests.
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Notes
- 1.
The bag file generated from this experiment is available at http://www.cra.com/robot_localization_ias13.zip.
References
J.J. Leonard and H.F. Durrant-Whyte, “Mobile robot localization by tracking geometric beacons,” Robotics and Automation, IEEE Transactions on vol. 7, no. 3, pp. 376–382, 1991.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler and A.Y. Ng, “ROS: an open-source robot operating system,” ICRA workshop on open source software vol. 3, no. 3.2, 2009.
G.L. Smith, S.F. Schmidt and L.A. McGee, “Application of statistical filter theory to the optimal estimation of position and velocity on board a circumlunar vehicle,” 1962.
R.E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Transactions of the ASME pp. 35–45, 1960.
G. Welch and G. Bishop, “An introduction to the Kalman filter,” 1995.
G.J. Bierman and C.L. Thornton, “Numerical comparison of Kalman filter algorithms: Orbit determination case study,” Automatica vol. 13, no. 1, pp. 23–35, 1977.
B.J. Odelson, M.R. Rajamani and J.B. Rawlings, “A new autocovariance least-squares method for estimating noise covariances,” Automatica vol. 42, no. 2, pp. 303–308, 2006.
P. Besl and N. McKay, “Method for registration of 3-D shapes,” IEEE PAMI, 14, pp. 239–256, 1992.
Microsoft Corporation, “Kinect for Windows,”. http://www.microsoft.com/en-us/kinectforwindows/, 2014
A. Milella, and R. Siegwart, “Stereo-based ego-motion estimation using pixel tracking and iterative closest point,” Computer Vision Systems, 2006 ICVS’06. IEEE International Conference on., 2006.
D. Stouch, A. Ost, T. Moore and C. Monnier, “Robust Tactical Communications Relay using Visual Object Detection on an Autonomous Mobile Robot,” International Advanced Robotics Programme’s 7th International Workshop on Robotics for Risky Environments - Extreme Robotics (IARP RISE-ER 2013) 2013.
M. Monajjemi, “Autonomylab/ardrone autonomy. github. com,” AutonomyLab/ardrone autonomy 2013.
S.J. Julier and J. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems,” AeroSense, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II 1997.
S. Thrun, D. Fox, W. Burgard and F. Dellaert, “Robust Monte Carlo Localization for Mobile Robots,” Artificial Intelligence vol. 128, no. 1–2, pp. 99–141, 2000.
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Moore, T., Stouch, D. (2016). A Generalized Extended Kalman Filter Implementation for the Robot Operating System. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_25
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DOI: https://doi.org/10.1007/978-3-319-08338-4_25
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