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Robust Free Space Detection in Occupancy Grid Maps by Methods of Image Analysis and Dynamic B-Spline Contour Tracking

Schreier, Matthias and Willert, Volker (2012):
Robust Free Space Detection in Occupancy Grid Maps by Methods of Image Analysis and Dynamic B-Spline Contour Tracking.
In: Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, Alaska, 16.-19. Sep. 2012, [Conference or Workshop Item]

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Schreier, Matthias and Willert, Volker
Title: Robust Free Space Detection in Occupancy Grid Maps by Methods of Image Analysis and Dynamic B-Spline Contour Tracking
Language: English
Title of Book: Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Event Location: Anchorage, Alaska
Event Dates: 16.-19. Sep. 2012
Date Deposited: 01 Oct 2012 11:06
Alternative Abstract:
Alternative abstract Language
We propose a new method for free space detection and description for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. The detection is based on successive morphological image processing steps that are applied to an occupancy grid map-based environment representation acquired by an automotive radar sensor. The boundary of the found free space segment is traced and serves as a virtual measurement for a time-variant Kalman Filter in order to estimate and track the control points of a two-dimensional B-spline closed free space contour over time. In contrast to existing free space detection methods, the proposed solution incorporates knowledge about the vehicle’s dimensions and does not exclude free space that is not directly in the line of sight, but mapped beforehand, as well as free space behind obstacles. Furthermore, the algorithm shows advantages in terms of an intuitive control over spatial and temporal smoothness of the solution as well as an inherent robustness due to model-based filtering. Moreover, the control points of the B-spline curve are proposed as a new low-dimensional representation of drivable free space of arbitrary shape. The effectiveness of the algorithm is demonstrated in real traffic scenarios.English
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