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Pregled bibliografske jedinice broj: 814138

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


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 (međunarodna recenzija, članak, znanstveni)


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

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

Izvornik
Robotics and autonomous systems (0921-8890) 83 (2016); 338-348

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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