Perception control for obstacle detection by a cross-country rover

Perception control consists of optimally tuning sensor or processing parameters in order to increase perception efficiency under requirement constraints or while adjusting to the environment. Perception control in the task of obstacle detection from cross-country navigation is addressed. The authors show how to maximize the vehicle safety at a given velocity, or inversely how to derive the maximum speed for a given safety. This optimization problems requires the joint analysis of how the vehicle velocity sets look-ahead requirements, how the computational cost of perception is related to the perception variables, the window of attention and image resolution, and how the reliability of the obstacle detection system is related to these variables. This criterion relies on experimental performance statistics. This system has been implemented and tested in outdoor operation.<<ETX>>

[1]  Malik Ghallab,et al.  Perception planning for a multi-sensory interpretation machine , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[2]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Steven A. Shafer,et al.  The Driving Pipeline: A Driving Control Scheme for Mobile Robots , 1990 .

[4]  Larry H. Matthies.  Toward stochastic modeling of obstacle detectability in passive stereo range imagery , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Sanjiv Singh,et al.  Obstacle detection for high speed autonomous navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[6]  Gregory D. Hager,et al.  Task-directed multisensor fusion , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[7]  Larry H. Matthies,et al.  Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.