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Semantic evidential grid mapping using monocular and stereo cameras

Richter, Sven 1; Wang, Yiqun 1; Beck, Johannes; Wirges, Sascha 1; Stiller, Christoph 1
1 Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT), Karlsruher Institut für Technologie (KIT)

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity, it is desired to add vision-based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline, including estimates for eight semantic classes, that is designed for straightforward fusion with range sensor data. Unlike other publications, our representation explicitly models uncertainties in the evidential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping results are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000133567
Veröffentlicht am 03.06.2021
DOI: 10.3390/s21103380
Zitationen: 2
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Karlsruher Institut für Technologie (KIT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000133567
Erschienen in Sensors
Verlag MDPI
Band 21
Heft 10
Seiten 3380
Schlagwörter autonomous driving; environment perception; grid mapping; stereo vision; monocular vision
Nachgewiesen in Web of Science
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