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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.
Larry Matthies,Paolo Bellutta, andCecilia 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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Larry Matthies, Paolo Bellutta, 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