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A Robust and Efficient 3D LADAR Odometer with Outlier Detection

Published online by Cambridge University Press:  28 September 2017

Zhen Zhu  and
Maarten Uijt de Haag
Zhen Zhu*
Affiliation:
(East Carolina University)
Maarten Uijt de Haag
Affiliation:
(Ohio University)
*
(E-mail: [email protected])
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Abstract

This paper discusses the estimation algorithms for Three-Dimensional (3D) displacement and 3D rotation using Two-Dimensional (2D) laser scanners. An efficient outlier detection method is proposed for both algorithms to help protect the integrity of navigation. The algorithms have been evaluated using both simulation and field test results. They are able to produce a robust odometry solution for an autonomous aircraft in an indoor environment.

Keywords

LaserNavigationRobust estimation
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 2 , March 2018 , pp. 317 - 338
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Bailey, T. and Durrant-Whyte, H. (2006). Simultaneous Localization and Mapping (SLAM): part II. IEEE Robotics & Automation Magazine, 13(3), 108–117.Google Scholar
Bates, D. and Van Graas, F. (2007). Covariance Analysis Considering the Propagation of Laser Scanning Errors for Use in LADAR Navigation. Proceedings of the 63rd Annual Meeting of The Institute of Navigation, Cambridge, MA.Google Scholar
Besl, P. and Mckay, N. (1992). A Method for Registration of 3D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (2), 239–256.CrossRefGoogle Scholar
Borges, G.A. and Aldon, M. J. (2004). Line Extraction in 2D Range Images for Mobile Robotics. Journal of Intelligent and Robotic Systems, 40, 267–297.Google Scholar
Champleboux, G., Lavallee, S., Szeliski, R. and Brunie, L. (1992). From accurate range imaging sensor calibration to accurate. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, IL, 83–89.Google Scholar
Chen, Y. and Medioni, G. (1991). Object Modeling by Registration of Multiple Range Images. IEEE International Conference on Robotics and Automation, 2724–2729.Google Scholar
Dellaert, F., Fox, D., Burgard, W. and Thrun, S. (1999). Monte Carlo localization for mobile robots. Proceedings IEEE International Conference on Robotics and Automation, Detroit, MI, 1322–1328.Google Scholar
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F. and Csorba, M. (2001). A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241.Google Scholar
Farrell, J. and Van Graas, F. (1991). Receiver Autonomous Integrity Monitoring (RAIM): Techniques, Performance and Potential. Proceedings of the 47th Annual Meeting of the Institute of Navigation, Williamsburg, VA.Google Scholar
Fischler, M.A. and Bolles, R.C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381–395.CrossRefGoogle Scholar
Fontanelli, D., Ricciato, L. and Soatto, S. (2007). A Fast RANSAC-Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements, 2007 IEEE International Conference on Automation Science and Engineering, Scottsdale, AZ, 597–602.CrossRefGoogle Scholar
Fox, D. (2003). Adapting the sample size in particle filters through KLD-sampling. The International Journal of Robotics Research, 22(12), 985–1003.Google Scholar
Grzonka, S., Grisetti, G. and Burgard, W. (2012). A Fully Autonomous Indoor Quadrotor. IEEE Transactions on Robotics, 28(1), 90–100.Google Scholar
Guivant, J. and Nebot, E. (2001). Optimization of the Simultaneous Localization and Map Building Algorithm for Real Time Implementation. IEEE Transactions on Robotics and Automation, 17(3), 242–257.CrossRefGoogle Scholar
Joerger, M., Jamoom, M., Spenko, M. and Pervan, B. (2016). Integrity of Laser-Based Feature Extraction and Data Association. Proceedings of IEEE/ION Position, Location, and Navigation Symposium (PLANS), Savannah, GA.Google Scholar
Lu, Z., Hu, Z. and Uchimura, K. (2009). SLAM Estimation in Dynamic Outdoor Environments: A Review. In:Xie, M., Xiong, Y., Xiong, C., Liu, H., Hu, Z. (eds) Intelligent Robotics and Applications. ICIRA 2009. Lecture Notes in Computer Science, 5928. Springer, Berlin, Heidelberg.Google Scholar
Meidow, J., Förstner, W. and Beder, C. (2009). Optimal Parameter Estimation with Homogeneous Entities and Arbitrary Constraints. Pattern Recognition Lecture Notes in Computer Science, 5748, 292–301.Google Scholar
Menq, C.-H., Yau, H.-T. and Lai, G.-Y. (1992). Automated Precision Measurement of Surface Profile in CAD-Directed Inspection. IEEE Transactions on Robotics and Automation, 8(2), 268–278.Google Scholar
Montemerlo, M. and Thrun, S. (2003). Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM. Proceedings of IEEE International Conference on Robotics and Automation, Taipei, Taiwan.Google Scholar
Shen, S., Michael, N. and Kumar, V. (2011). Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained MAV. Proceedings of IEEE Conference on Robotics and Automation, Shanghai, China.Google Scholar
Soloviev, A. and Uijt de Haag, M. (2010a). Three-Dimensional Navigation with Scanning Ladars: Concept & Initial Verification. IEEE Transactions on Aerospace and Electronic Systems, 46(1), 14–31.CrossRefGoogle Scholar
Soloviev, A. and Uijt de Haag, M. (2010b). Monitoring of Moving Features in Laser Scanner-Based Navigation. IEEE Transactions on Aerospace and Electronic Systems, 46(4), 1699–1715.CrossRefGoogle Scholar
Soloviev, A., Bates, D. and Van Graas, F. (2007). Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution. NAVIGATION, Journal of the Institute of Navigation, 54(3), 189–205.CrossRefGoogle Scholar
Thrun, S., Burgard, W. and Fox, D. (2005). Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). Cambridge: MIT Press.Google Scholar
Vadlamani, A. K. and Uijt de Haag, M. (2006). Use of Laser Range Scanners for Precise Navigation in Unknown Environments. Proceedings of the International Technical Meeting of The Satellite Division of the Institute of Navigation, Fort Worth, Texas.Google Scholar
Zhang, Z. and Faugeras, O. (1991). Determining motion from 3D line segment matches: a comparative study. Image and Vision Computing, 9(1), 10–19.Google Scholar
Zhu, Z., and Uijt de Haag, M. (2015). Integrity Analysis in 3D LADAR Odometry. Proceedings of the International Technical Meeting of The Satellite Division of the Institute of Navigation, Tampa, Florida.Google Scholar