A MULTI-SENSOR-BASED NAVIGATION FRAMEWORK FOR INTELLIGENT VEHICLE
Abstract
This paper presents a platform-independent framework for autonomous navigation of an intelligent vehicle. The framework consists of three integrated modules, namely; waypoint navigation, obstacle localization and path planning. Each module has been individually validated based on experiments with a real intelligent vehicle. For waypoint navigation, we propose the use of Google Earth for generation of reference waypoints and a simple bias subtraction method for GPS calibration. Based on the autonomous navigation experiments, this method yields a more stable navigation path compared to the use of GPS-generated waypoints and translational error can be efficiently eliminated. For obstacle localization, we develop a feature-based approach for obstacle detection and map generation based on the use of compactness measure and perspective projections. With an integrated use of a camera, digital compass, and GPS, static obstacles of a known dimension, along with their positions and orientations on the road can be calculated in real-time while the vehicle is travelling. Based on the derived information, an overhead-view obstacle map is generated to provide an internal representation of the road. The experiment on an unmarked road shows that the estimation of an obstacle can be achieved with maximum errors of 1.4 degree, 15 cm and 12 cm in angle, depth and lateral positions, respectively. Based on the obstacle map and the Google Earth waypoints, artificial potential field is adopted for collision-free path generation.
References
Ballard, D.H. and Brown, C.M. (1982).Computer Vision Prentice-Hall, Englewood Cliffs, NJ, USA.
Bribiesca, E. (1997). Measuring 2-D shape compactness using the contact perimeter.Computers and Mathematics with Applications, 33(11):1-9.
Carlbom, I. and Paciorek, J. (1978). Planar geometric projections and viewing transformations.ACM Computing Surveys(CSUR), 10(4):502
.Cui, Y.J. and Ge, S.S. (2003). Autonomous vehicle positioning with GPS in urban canyon environments. IEEE Transactions on Robotics and Automation, 19(1):15-25.
Deelertpaiboon, C. and Parnichkun, M. (2008).Fusion of GPS, compass, and camera forlocalization of an intelligent vehicle.International Journal of Advanced Robotic Systems, 5(4):315-326.
Gonzalez, R.C. and Richard, E.W. (1992).Digital image processing. Addison Wesley.
Introduction - Google Earth user guide. (2009)Available from: http://earth.google.com/userguide/v4/.
Hayashi, M., Tanaka, T., and Yonekawa, M.(2008). High accuracy positioning of two-wheeled vehicle at high speed traveling using GPS. the SICE Annual Conference on Instrumentation, Control and Information Technology, Tokyo, Japan, p. 3500-3503.
Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J. (1993). Global positioning system. Theory and Practice.
Limsoonthrakul, S., Dailey, M.N., andParnichkun, M. (2009). Intelligent vehicle localization using GPS, compass, and machine vision. the IEEE/RSJ International Conference on Intelligent Robots and Systems, Missouri, USA, p. 3981-3986
Miller, I., Campbell, M., Huttenlocher, D.,Kline, F.R., Nathan, A., Lupashin, S.,Catlin, J., Schimpf, B., Moran, P., and Zych, N. (2008). Team Cornell’s Skynet:Robust perception and planning in anurban environment. Journal of Robotic Systems, 25(8):493-527.
Naranjo, J.E., Gonzalez, C., Garcia, R., Pedro,T.E., Revuelto, J., and Reviejo, J. (2004).Fuzzy logic based lateral control for GPSmap tracking. the IEEE Intelligent Vehicle Symposium, Parma, Italy, p. 397-400.
Passino, K.M. (2004). Biomimicry for optimization, control, and automation.Springer Verlag.
Ratsamee, P., Surapa, T., and Kondo, T. (2010).An enhancement of waypoint navigation system for intelligent vehicle using Google Earth. the International Conference on Information and Communication Technology for Embedded Systems, Pathum Thani, Thailand.
Richard, B. (2005). Intelligent vehicle technology and trends. Artech House.
Siciliano, B. and Khatib, O. (2008). Springer handbook of robotics. Springer-Verlag New York Inc.
Sukkarieh, S., Nebot, E.M., and Durrant-Whyte, H.F. (1999). A high integrityIMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation, 15(3):572-578.
Thailand Intelligent Vehicle Challenge. (2007).Available from: http://tivc.ait.ac.th.
Thrun, S., Montemerlo, M., Dahlkamp, H.,Stavens, D., Aron, A., Diebel, J., Fong,P., Gale, J., Halpenny, M. and Hoffmann,G. (2006). Stanley: The robot that won the DARPA Grand Challenge. Journal of Field Robotics, 23(9):661-692.
Vincent, L. (1992). Morphological area openings and closings for grey-scale images. Shape in Picture: Mathematical Description of Shape in Grey-level Images, p. 197-208.
Webber, F. (2003). Statement of the alliance of automobile manufacturers. Presented before the Commerce, Science and Transportation Committee of the U.S.Senate, Noverber 15, 2005.
Xiangdong, L., Kirubarajan, T., Bar-Shalom,Y., and Xiaorong, L. (2001). Enhanced accuracy GPS navigation using the interacting multiple model estimator. theIEEE Aerospace Conference, Big Sky, MT , USA, p. 1911-1923.
