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3D simultaneous localization and mapping using IMU and its observability analysis

Published online by Cambridge University Press:  09 December 2010

Farhad Aghili
Farhad Aghili*
Affiliation:
Canadian Space Agency (CSA), Space Exploration, 6767 route de l'Aeroport, Quebec J3Y 8Y9, Canada
*
*Corresponding author. Email: farhad.aghili@asc-csa.gc.ca
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Summary

This paper investigates 3-dimensional (3D) Simultaneous Localization and Mapping (SLAM) and the corresponding observability analysis by fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF). In addition to the vehicle's states and landmark positions, the self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix and the observation sensitivity matrix are derived in closed form, making it suitable for real-time implementation. Examination of the observability of the 3D SLAM system leads to the the conclusion that the system remains observable, provided that at least three known landmarks, which are not placed in a straight line, are observed.

Keywords

Robot localizationNavigationPose estimation and registrationSLAMMobile robots

Information

Type
Articles
Information
Robotica , Volume 29 , Issue 6 , October 2011 , pp. 805 - 814
Copyright
Copyright © Cambridge University Press 2010

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References

1.Barshan, B. and Durrant-Whyte, H. F., “Inertial navigation systems for mobile robots,” IEEE Trans. Robot. Autom. 11 (3), 328–342 (Jun. 1995).CrossRefGoogle Scholar
2.Vaganay, J., Aldon, M. J. and Fournier, A., “Mobile Robot Attitude Estimation by Fusion of Inertial Data,” Proceedings of the IEEE International Conference on Robotics & Automation, Atlanta, GA (May 1993) pp. 277–282.Google Scholar
3.Dissanayake, G., Sukkarieh, S., Nebot, E. and Durrant-Whyte, H., “The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications,” IEEE Trans. Robot. Autom. 17 (5), 731–747 (2001).CrossRefGoogle Scholar
4.Choi, H.-S., Park, O.-D. and Kim, H.-S., “Autonomous Mobile Robot Using GPS,” Proceedings of the International Conference on Control & Automation, Budapest, Hungary (Jun. 2005) pp. 858–862.Google Scholar
5.Aghili, F. and Salerno, A., “Attitude Determination and Localization of Mobile Robots Using Two RTK GPSs and IMU,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots & Systems, St. Louis, MO (Oct. 2009) pp. 2045–2052.CrossRefGoogle Scholar
6.Strelow, D. and Singh, S., “Online Motion Estimation from Image and Inertial Measurements,” Proceedings of the International Conference on Advanced Robotics, Portugal (Jun. 30–Jul. 3 2003).Google Scholar
7.Mallet, A., Lacroix, S. and Gallo, L., “Position Estimation in Outdoor Environments using Pixel Tracking and Stereovision,” Proceedings of the IEEE International Conference on Robotics & Automation, San Francisco, CA (Apr. 2000) pp. 3519–3524.Google Scholar
8.Lamon, P. and Siegwart, R., “3D position tracking in challenging terrain,” Int. J. Robot. Res. 26 (2), 167–186 (Feb. 2007).Google Scholar
9.Mourikis, A. I., Trawny, N., Roumeliotis, S. I., Helmick, D. M. and Matthies, L., “Autonomous stair climbing for tracked vehicles,” Int. J. Robot. Res. 26 (7), 737–758 (Jul. 2007).CrossRefGoogle Scholar
10.Smith, R., Self, M. and Cheesman, P., Estimating Uncertain Spatial Relationship in Robotics, Autonomous Robot Vehicle (Cox, I. and Wilfong, G., eds.) (Springer-Verlag, New York, 1987).Google Scholar
11.Durant-Whyte, H. F., “Uncertain geometry in robotics,” IEEE Trans. Robot. Autom. 4 (1), 23–31 (1988).CrossRefGoogle Scholar
12.Ayache, N. and Faugeras, O., “Maintaining a representation of the environment of a mobile robot,” IEEE Trans. Robot. Autom. 5 (6), 804–819 (1998).CrossRefGoogle Scholar
13.Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous Map Building and Localization for an Autonomous Mobile Robot,” Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems, Osaka, Japan (Nov. 1991) pp. 1442–1447.Google Scholar
14.Bailey, T. and Durrant-Whyte, H., “Simultaneous localization and mapping (SLAM): Part II,” IEEE Robot. Autom. Mag. 13 (3), 108–117 (Sep. 2006).CrossRefGoogle Scholar
15.Xi, B., Guo, R., Sun, F. and Huang, Y., “Simulation Research for Active Simultaneous Localization and Mapping Based on Extended Kalman Filter,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Qingdao, China (Sep. 2008) pp. 2443–2448.Google Scholar
16.Rehbinder, H. and Ghosh, B. K., “Pose estimation using line-based dynamic vision and inertial sensors,” IEEE Trans. Autom. Control 48 (2), 186–199 (Feb. 2003).CrossRefGoogle Scholar
17.Thienel, J. and Sanner, R. M., “A Coupled Nonlinear Spacecraft Attitude Controller and Observer with an Unknown Constant Gyro Bias and Gyro Noise,” IEEE Trans. Autom. Control 48 (11), 2011–2015 (Nov. 2003).Google Scholar
18.Vasconcelos, J., Cunha, R., Silvestre, C. and Oliveira, P., “Landmark-Based Nonlinear Observer for Rigid Body Attitude and Position Estimation,” Proceedings of the 46th IEEE Conference on Decision and Control, 2007, New Orleans, LA (Dec. 12–14 2007) pp. 1033–1038.CrossRefGoogle Scholar
19.Amd, K. W. L., Wijesoma, W. S. and Guzman, J. I., “On the Observability and Observability Analysis of SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 3569–3574.Google Scholar
20.Perera, L., Melkumyan, A. and Nettleton, E., “On the Linear and Nonlinear Observability Analysis of the Slam Problem,” Proceedings of the ICM 2009: IEEE International Conference on Mechatronics, 2009, Malaga, Spain (Apr. 14–17, 2009) pp. 1–6.Google Scholar
21.Marino, R., Santosuesso, G. L. and Tomei, P., “Robust adaptive observers for nonlinear systems with bounded disturbances,” 38th IEEE Conference on Decision and Control, Phoenix, AZ, USA, vol. 5, (1999) pp. 5200–5205.Google Scholar
22.Zhang, Q., “Adaptive observer for multiple-input-multiple-output (mimo) linear time-varying systems,” Autom. Control, IEEE Trans. 47 (3), 525–529 (Mar. 2002).CrossRefGoogle Scholar
23.Chen, Z., Jiang, K. and Hung, J. C., “Local Observability Matrix and Its Application to Observability Analysis,” Proceedings of the 16th Annual Conference of IEEE (IECON'90), Pacific Grove, CA (Nov. 1990) pp. 100–103.Google Scholar
24.Andrade-Cetto, J. and Sanfeliu, A., “The effects of partial observability when building fully correlated maps,” IEEE Trans. Robot. Autom. 21 (4), 771–777 (2005).Google Scholar
25.Huang, S. and Dissanayake, G., “Convergence Analysis for Extended Kalman Filter Based SLAM,” Proceedings of the IEEE International Conference on Robotics & Automation, Orlando, FL (May 2006) pp. 1556–1563.Google Scholar
26.Huang, G. P., Mourikis, A. I. and Roumeliotis, S. I., “Analysis and Improvement of Consistency of Extended Kalman Filter Based SLAM,” Proceedings of the IEEE International Conference on Robotics & Automation, Pasadena, CA (May 2008) pp. 473–479.Google Scholar
27.Surmann, H., Nuchter, A., Lingemann, K. and Hetzberg, J., “6D SLAM – Preliminary Report on Closing the Loop in Six Dimension,” Proceedings of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles (IVA), Lisbon, Portugal (Jul. 2004).Google Scholar
28.Kim, J. and Sukkarieh, A., “Autonomous airborne navigation in unknown terrain environment,” IEEE Trans. Aerosp. Electron. Syst. 40 (3), 1031–1045 (Jul. 2004).Google Scholar
29.Weingarten, J. and Siegwart, R., “EKF-based 3D SLAM for Structured Environment Reconstruction,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 2089–2094.Google Scholar
30.Cole, D. M. and Newman, P., “Using Laser Range Data for 3D SLAM in Outdoor Environment,” Proceedings of the IEEE International Conference on Robotics & Automation, Orlando, FL (May 2006) pp. 1556–1563.Google Scholar
31.Nuechter, A., Lingemann, K., Hertzberg, J. and Surmann, H., “6D SLAM–Mapping Outdoor Environment,” Proceedings of the IEEE International Workshop of Safety, Security and Rescue Robotics, Gaithersburg, MD (Aug. 2006).Google Scholar
32.Kim, J. and Sukkarieh, S., “Real-time implementation of airborne inertial-SLAM,” Robot. Auton. Syst. 55 (1), 519–535 (2007).CrossRefGoogle Scholar
33.Abdallah, S., Asmar, D. and Zelek, J., “A benchmark for outdoor SLAM systems,” J. Field Robot. 24 (1–2), 145–165 (2007).CrossRefGoogle Scholar
34.Bryson, M. and Sukkarieh, S., “Observability analysis and active control for airborne slam,” Aerosp. Electron. Syst. IEEE Trans. 44 (1), 261–280 (Jan. 2008).CrossRefGoogle Scholar
35.Liu, Z., Hu, Z. and Uchimura, K., “SLAM estimation in dynamic outdoor environment: A review,” Lecture Notes Comput. Sci. 5928 (2009).CrossRefGoogle Scholar
36.Nemra, A. and Aouf, N., “Robust airborne 3D visual simultaneous localization and mapping with observability and consistency analysis,” J. Intell. Robot. Syst. 55 (4–5), 345–376 (2009).CrossRefGoogle Scholar
37.Lefferts, E. J., Markley, F. L. and Shuster, M. D., “Kalman filtering for spacecraft attitude estimation,” J. Guid. 5 (5), 417–429 (Sep.–Oct. 1982).CrossRefGoogle Scholar
38.Pittelkau, M. E., “Kalman filtering for spacecraft system alignment calibration,” J. Guid., Control Dyn. 24 (6), 1187–1195 (Nov. 2001).CrossRefGoogle Scholar
39.Hermann, R. and Krener, A., “Nonlinear controllability and observability,” Autom. Control, IEEE Trans. 22 (5), 728–740 (Oct. 1977).CrossRefGoogle Scholar
40.Goshen-Meskin, D. and Bar-Itzhack, I. Y., “Observability analysis of piece-wise constant systems. i. theory,” Aerosp. Electron. Syst., IEEE Trans. 28 (4), 1056–1067 (Oct. 1992).Google Scholar
41.Maybeck, P. S., Stochastic Models, Estimation, and Control (Vol. 2) (Academic Press, New York, 1982).Google Scholar
42.Chui, C. K. and Chen, G., Kalman Filtering with Real-Time Applications (Springer, Berlin, 1998) pp. 113–115.Google Scholar