Conference Paper

Bots2ReC: Radar-SLAM für die Teilautonome Asbestsanierung

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Abstract

Im Rahmen des EU-Forschungsprojekts Bots2ReC entwickelt das IGMR der RWTH Aachen University in Kooperation mit Partnern aus Forschung und Industrie ein (teil-)autonomes robotisches System zur Dekontaminierung asbestbefal-lener Gebäude. Eine grundlegende Fragestellung des Projekts adressiert die Kartierung der Umgebung und die Loka-lisierung der mobilen Einheiten, sog. Simultaneous Localization and Mapping (SLAM). Auch wenn das ursprüngliche SLAM-Problem in der Literatur umfänglich behandelt wurde, erfordern die schlechten Sichtverhältnisse infolge der As-bestentfernung neuartige Perzeptions-und Datenverarbeitungsansätze. Daher wird für die Umfelderfassung anstelle der sonst üblichen Laser-Sensoren ein Radar-Sensor eingesetzt. Dieser ist zwar robuster gegen schlechte Sichtverhältnisse als konventionelle Laser-Sensoren, hat jedoch eine geringere Genauigkeit bei der Distanz-und Winkelmessung sowie ein wesentlich höheres Datenaufkommen. Der vorgestellte probabilistische Radar-SLAM-Algorithmus trägt diesen Gege-benheiten Rechnung und ermöglicht die effiziente Verarbeitung der Radar-Messdaten. Eine abschließende experimentelle Validierung belegt die Funktion und Robustheit dieses Algorithmus sowohl in einer Testumgebung als auch in einer realen Anwendungsumgebung. --------------------- In the EU-funded research project Bots2ReC, the IGMR of RWTH Aachen University, together with partners in rese-ach and industry, develop a semi-autonomeous robotic system for the decontamination of Asbestos-polluted buildings. A fundamental task in Bots2ReC is to map the environment and localize the mobile unit simultaneously, so called Simultaneous Localization and Mapping (SLAM). Even though, in literature the SLAM problem is considered to be solved, in Bots2ReC the dusty environment and its restricted visibility constitute new challenges to the common environmental cognition and sensor data handling approaches. Therefore, the Bots2ReC robot utilizes radar sensors for environmental cognition. These sensors are more robust in environments with restricted visibility than conventional ranging sensors, but yield less accurate measurements of distance and angle, while producing more data points per reading. In this paper, a probabilistic radar SLAM approach is proposed, which allows for efficient data handling. Concluding, an experimental validation proves the feasibility and robustness of the proposed algorithms in artificial and real world environments.

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