DocumentCode :
2239134
Title :
Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors
Author :
Matthies, Larry ; Grandjean, Pierrick
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
fYear :
1993
fDate :
15-17 Jun 1993
Firstpage :
657
Lastpage :
658
Abstract :
Statistical modeling and evaluation of the performance of obstacle detection systems for unmanned ground vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. This issue is addressed for imaging range sensors by dividing the valuation problem into two levels, i.e., the quality of the range data itself and the quality of the obstacle detection algorithms applied to the range data. Existing models of the quality of range data from stereo vision and AM-CW laser range-finders (LADAR) are reviewed. These are used to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as functions of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. These models are evaluated experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors
Keywords :
image sensors; laser ranging; measurement by laser beam; mobile robots; probability; stereo image processing; vehicles; detection probability; false alarm probability; gravel road; imaging range sensors; noise level; obstacle detectability; obstacle distance; obstacle size; performance evaluation; sensor resolution; statistical modeling; stereo image pairs; stochastic performance modeling; unmanned ground vehicles; Cost accounting; Detection algorithms; Image sensors; Land vehicles; Laser modes; Laser radar; Sensor systems; Stereo vision; Stochastic processes; Vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on
Conference_Location :
New York, NY
ISSN :
1063-6919
Print_ISBN :
0-8186-3880-X
Type :
conf
DOI :
10.1109/CVPR.1993.341041
Filename :
341041
Link To Document :
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