Conference Paper

Bots2ReC: Radar-SLAM für die Teilautonome Asbestsanierung

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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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The “Robots to Re-Construction” Bots2ReC project was launched in 02/2016 to introduce mobile robotic units on construction sites for the automated removal of asbestos contamination. The novel use case of asbestos removal and the dedicated robotic system are introduced. After giving the motivation for the use case, a brief overview of recent approaches to mobile robotic systems on construction sites is given. Analysis of the use case and the situation of asbestos contamination in Europe follow. The project approach is shown, and specific challenges regarding mobility, perception, and collaboration are analyzed. The publication concludes with a brief description of the current prototype and an introduction of the project consortium. As we are addressing the first special edition of this journal, our focus is on the introduction of the use case, challenges, and approach rather than on specific scientific or technological solutions, that will follow during the course of the project.
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Simultaneous Localization and Mapping (SLAM) consists in the concurrent construction of a representation of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. The paper serves as a tutorial for the non-expert reader. It is also a position paper: by looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: do robots need SLAM? Is SLAM solved?
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