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izvor podataka: crosbi

Radar and stereo vision fusion for multitarget tracking on the special Euclidean group (CROSBI ID 227854)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Ćesić, Josip ; Marković, Ivan ; Cvišić, Igor ; Petrović, Ivan Radar and stereo vision fusion for multitarget tracking on the special Euclidean group // Robotics and autonomous systems, 83 (2016), 338-348. doi: 10.1016/j.robot.2016.05.001

Podaci o odgovornosti

Ćesić, Josip ; Marković, Ivan ; Cvišić, Igor ; Petrović, Ivan

engleski

Radar and stereo vision fusion for multitarget tracking on the special Euclidean group

Reliable scene analysis, under varying conditions, is an essential task in nearly any assistance or autonomous system application, and advanced driver assistance systems (ADAS) are no exception. ADAS commonly involve adaptive cruise control, collision avoidance, lane change assistance, traffic sign recognition, and parking assistance—with the ultimate goal of producing a fully autonomous vehicle. The present paper addresses detection and tracking of moving objects within the context of ADAS. We use a multisensor setup consisting of a radar and a stereo camera mounted on top of a vehicle. We propose to model the sensors uncertainty in polar coordinates on Lie Groups and perform the objects state filtering on Lie groups, specifically, on the product of two special Euclidean groups, i.e., SE(2) 2 . To this end, we derive the designed filter within the framework of the extended Kalman filter on Lie groups. We assert that the proposed approach results with more accurate uncertainty modeling, since used sensors exhibit contrasting measurement uncertainty characteristics and the predicted target motions result with banana-shaped uncertainty contours. We believe that accurate uncertainty modeling is an important ADAS topic, especially when safety applications are concerned. To solve the multitarget tracking problem, we use the joint integrated probabilistic data association filter and present necessary modifications in order to use it on Lie groups. The proposed approach is tested on a real-world dataset collected with the described multisensor setup in urban traffic scenarios.

advanced driver assistance systems ; detection and tracking of moving objects ; joint integrated probabilistic data association ; radar ; stereo camera

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Podaci o izdanju

83

2016.

338-348

objavljeno

0921-8890

10.1016/j.robot.2016.05.001

Povezanost rada

Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

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