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14 June 2023 Modeling and evaluation of thermal stereo vision for off-road driving (Conference Presentation)
Larry Matthies, Paolo Bellutta, Cecilia R. Mauceri
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Proceedings Volume 12549, Unmanned Systems Technology XXV; 1254908 (2023) https://doi.org/10.1117/12.2663421
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
The use of passive sensors to minimize signature in night-time autonomous driving of robotic ground vehicles has been a goal for roughly three decades. Demonstrations have been done in the past at low to moderate speeds with vehicles using stereo pairs of thermal cameras for 3-D perception; however, there has never been an end-to-end model of the probability of mission success in this application, defined here as the probability of colliding with an obstacle and the expected rate of false obstacle detections as a function of distance traveled and other relevant parameters. We integrate and extend prior work on modeling the performance of 3-D perception for obstacle detection with thermal stereo vision to provide the first such model, We include experimental results with a stereo vision algorithm based on a deep neural network (“deep stereo”) on LWIR stereo images and on synthetically generated LWIR and visible stereo images to characterize key elements of sensor performance.
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Larry Matthies, Paolo Bellutta, and Cecilia R. Mauceri "Modeling and evaluation of thermal stereo vision for off-road driving (Conference Presentation)", Proc. SPIE 12549, Unmanned Systems Technology XXV, 1254908 (14 June 2023); https://doi.org/10.1117/12.2663421
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KEYWORDS
Visual process modeling
3D modeling
Thermal modeling
Long wavelength infrared
Mathematical modeling
Performance modeling
Evolutionary algorithms
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