Unscented multi-point smoother for fusion of delayed displacement measurements: Application to agricultural robots

Modeling, Identification and Control

Volumn 38, Issue 1, 2017, Pages 1-9

  Arbo, Mathias Hauan ^a   Utstumo, Trygve ^a,b   Brekke, Edmund ^a   Gravdahl, Jan Tommy ^a  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b Adigo AS Langhus   (Norway)

Author keywords

Agricultural Robotics; Robot Navigation; Sensor fusion

Indexed keywords

AGRICULTURE; BAYESIAN NETWORKS; DISPLACEMENT MEASUREMENT; ROBOTICS;

AGRICULTURAL ROBOT; AGRICULTURAL ROBOTICS; BAYESIAN FORMULATION; GPS MEASUREMENTS; ROBOT NAVIGATION; ROBOTIC NAVIGATION; SENSOR FUSION; UNSCENTED KALMAN FILTER;

ROBOTS;

EID: 85018429077     PISSN: 03327353     EISSN: 18901328     Source Type: Journal    
DOI: 10.4173/mic.2017.1.1     Document Type: Article

References (23)

1. Biber, P., Weiss, U., Dorna, M., and Albert, A. Navigation System of the Autonomous Agricultural Robot "BoniRob". Workshop on Agricultural Robotics: Enabling Safe, Efficient, and Affordable Robots for Food Production, 2012. pages 1-7.
2. Bogue, R. Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 2016. 43(5):450-456. doi:10. 1108/IR-05-2016-0142.
3. Cruz, C. D. L. And Carelli, R. Dynamic Modeling and Centralized Formation Control of Mobile Robots. In IECON 2006-32nd Annual Conference on IEEE Industrial Electronics. pages 3880-3885, 2006. doi:10. 1109/IECON. 2006. 347299.
4. Forster, C., Pizzoli, M., and Scaramuzza, D. SVO: Fast semi-direct monocular visual odometry. Proceedings-IEEE International Conference on Robotics and Automation, 2014. pages 15-22. doi:10. 1109/ICRA. 2014. 6906584.
5. Goldberg, S. B. And Matthies, L. Stereo and IMU assisted visual odometry on an OMAP3530 for small robots. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2011. doi:10. 1109/CVPRW. 2011. 5981842.
6. Grimstad, L., Pham, C. D., Phan, H. T., and From, P. J. On the design of a low-cost, light-weight, and highly versatile agricultural robot. Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO, 2016. 2016-March(October). doi:10. 1109/ARSO. 2015. 7428210.
7. Grimstad, L., Phan, H. N. T., Pham, C. D., and Bjugstad, N. Initial field-testing of Thorvald, a versatile robotic platform for agricultural applications. IEEE International Workshop on Advanced Robotics and its Social Impacts, 2015.
8. Jørgensen, R. N., Sørensen, C. G., Pedersen, J. M., Havn, I., Jensen, K., Søgaard, H. T., and Sørensen, L. Hortibot: A system design of a robotic tool carrier for high-tech plant nursing. CIGR Ejournal, 2007. IX:Manuscript ATOE 07 006.
9. Julier, S. J. And Uhlmann, J. K. New extension of the kalman filter to nonlinear systems. 1997. doi:10. 1117/12. 280797.
10. Julier, S. J. And Uhlmann, J. K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), 2002. 2:887-892 vol. 2. doi:10. 1109/ACC. 2002. 1023128.
11. Martins, F. Velocity-based dynamic model and adaptive controller for differential steered mobile robot. Mathworks Fileexchange 44850, 2013.
12. Van der Merwe, R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Ph. D. Thesis, Oregon Health & Science University, 2004.
13. Van der Merwe, R. And Wan, E. A. Sigma-Point Kalman Filters for Integrated Navigation. In Proceedings of the 60th Annual Meeting of the Institute of Navigation (ION). pages 641-654, 2004.
14. Molstad, L., Reent Köster, J., Bakken, L., Dörsch, P., Lien, T., Overskeid, Ø., Utstumo, T., Løvås, D., and Brevik, A. A field robot for autonomous laser-based n2o flux measurements. In EGU General Assembly Conference Abstracts, volume 16. page 16109, 2014.
15. Mourikis, A. I. And Roumeliotis, S. I. A multi-state constraint Kalman filter for vision-aided inertial navigation. Proceedings-IEEE International Conference on Robotics and Automation, 2007. pages 3565-3572. doi:10. 1109/ROBOT. 2007. 364024.
16. Mourikis, A. I., Roumeliotis, S. I., and Burdick, J. W. SC-KF mobile robot localization: A stochastic cloning Kalman filter for processing relative-state measurements. IEEE Transactions on Robotics, 2007. 23(4):717-730. doi:10. 1109/TRO. 2007. 900610.
17. Mourikis, A. I., Trawny, N., Roumeliotis, S. I., Johnson, A. E., Ansar, A., and Matthies, L. Visionaided inertial navigation for spacecraft entry, descent, and landing. IEEE Transactions on Robotics, 2009. doi:10. 1109/TRO. 2009. 2012342.
18. Romanovas, M., Schwarze, T., Schwaab, M., and Traechtler, M. Stochastic Cloning Kalman Filter for Visual Odometry and Inertial / Magnetic Data Fusion. Information Fusion (FUSION), 2013, 2013. pages 1434-1441.
19. Slaughter, D. C., Giles, D. K., and Downey, D. Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 2008. 61(1):63-78. doi:10. 1016/j. compag. 2007. 05. 008.
20. Thrun, S., Burgard, W., and Fox, D. Probabilistic robotics. MIT press, 2005.
21. Urdal, F., Utstumo, T., Vatne, J. K., Ellingsen, S. A. A., and Gravdahl, J. T. Design and control of precision drop-on-demand herbicide application in agricultural robotics. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. IEEE, Singapore, pages 1689-1694, 2014. doi:10. 1109/ICARCV. 2014. 7064570.
22. Utstumo, T., Berge, T. W., and Gravdahl, J. T. Nonlinear model predictive control for constrained robot navigation in row crops. 2015 IEEE International Conference on Industrial Technology (ICIT), 2015. (July):357-362. doi:10. 1109/ICIT. 2015. 7125124.
23. Utstumo, T. And Gravdahl, J. T. Implementation and comparison of attitude estimation methods for agricultural robotics. IFAC Proceedings Volumes (IFACPapersOnline), 2013. 4(PART 1):52-57. doi: 10. 3182/20130828-2-SF-3019. 00051.