Adaptive complementary filtering algorithm for mobile robot localization
Journal of the Brazilian Computer Society volume 15, pages 19–31 (2009)
As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot’s motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.
Baerveldt AJ and Klang R. A low-cost and low-weight attitude estimation systemfor a autonomous helicopter. In:Proceedings of IEEE International Conference on Intelligent Engineering Systems; 1997; Budapest, Hungary. IEEE; 1997.
Coppersmith D and Winograd S. Matrix multiplicationvia arithmetic progressions. In:Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing; 1987; New York. ACM; 1987. p. 1–6.
Euston M, Coote P, Mahony R, Kim J and Hamel T. A complementary filter for attitude estimation of a fixed-wing uav. In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems; 2008; Nice, France. IEEE; 2008. p. 340–345.
Google Maps API.Google Code. [on the internet] 2008. Available from: http://code.google.com/apis/maps/. Access in: 29/07/2008.
Günthner W.Enhancing cognitive assistance systems with inertial measurement units. New York: Springer; 2008.
Iscold P, Oliveira GRC, Neto AA, Pereira GAS and Torres LAB. Desenvolvimento de horizonte artificial para aviação geral baseado em sensores MEMS. In:Anais do 5 Congresso Brasileiro de Engenharia Inercial; 2007; Rio de Janeiro.
Jeffrey SJ and Uhlmann K. A new extension of the kalman filter to nonlinear systems. In:Proceedings of the 11 International Symposium on Aerospace/Defense Sensing, Simulation and Controls; 1997; Orlando, Florida.International Society for Optical Engineering; 1997.
Li Y, Wang J, Rizos C, Mumford P and Ding W. Low-cost tightly coupledgps/ins integration based on a nonlinear kalman filtering design. In:Proceedings of the National Technical Meeting of the Institute of Navigation; 2006, San Diego. p. 958–966.
Liu B, Adams M and Ibañez-Guzmán J. Multi-aided inertial navigation for ground vehicles in outdoor uneven environments. In:Proceedings of IEEE International Conference on Robotics and Automation; 2005; Barcelona, Espanha. IEEE; 2005.
Mahony R and Hamel T.Advances in unmanned aerial vehicles: state of the art and the road to autonomy, chapter robust nonlinear observers for attitude estimation of mini uavs. New York: Springer; 2007. p. 343–376.
Müller M, Surmann H, Pervölz K and May S. The accuracy of 6D SLAM using the AIS 3D laser scanner. In:Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems; 2006; Heidelberg, Germany.
Ojeda L and Borenstein J.Improved position estimation for mobile robots on rough terrain using attitude information. Michigan: The University of Michigan; 2001. (Technical report)
Sukkarieh S, Nebot EM and Durrant-Whyte HF. A high integrity IMU/GPS navigation loop for autonomous land vehicle applications.IEEE Transactions on Robotics and Automation. 1999; 15(6):572–578.
Thrun S, Burgard W and Fox D.Probabilistic robotics: intelligent robotics and autonomous agents. Cambridge, MA: The MIT Press; 2005.
Thrun S, Diel M, and Hähnel D. Scan alignment and 3D surface modeling with a helicopter platform. In:Proceedings of the International Conference on Field and Service Robotics; 2003; Lake Yamanaka, Japan.
Walchko KJ and Mason PAC.Inertial Navigation. In:Proceedings of Florida Conference on Recent Advances in Robotics; 2002; Miami, Florida.
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Neto, A.A., Macharet, D.G., da Silva Campos, V.C. et al. Adaptive complementary filtering algorithm for mobile robot localization. J Braz Comp Soc 15, 19–31 (2009). https://doi.org/10.1007/BF03194503
- complementary filtering
- outdoor navigation
- mobile robots