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Grid Mapping in Dynamic Road Environments: Classification of Dynamic Cell Hypothesis via Tracking

Schreier, Matthias and Willert, Volker and Adamy, Jürgen (2014):
Grid Mapping in Dynamic Road Environments: Classification of Dynamic Cell Hypothesis via Tracking.
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), In: IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, May 31 – June 7, [Conference or Workshop Item]

Abstract

We propose a method capable of acquiring an occupancy grid map-based representation of the local, static driving environment around an intelligent vehicle in the presence of dynamic objects. These corrupt the representation due to violating the underlying static-world assumptions of common grid mapping algorithms and are therefore detected and filtered from the map. For this purpose, a subsequent step is suggested that identifies, clusters and merges dynamic cell hypothesis in a novel way. Thereafter, an Interacting-Multiple-Model-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) tracker is used to classify of whether cell movements behave consistently with possible movement characteristics of real dynamic objects or are just generated by noise or newly observed static environment. In opposition to many other approaches, the method explicitly combines information of newly occupied and free areas, completes the shape of only partly visible dynamic objects and uses an advanced object tracking scheme to clean the grid from dynamic object corruptions. The method is evaluated with grids generated by an automotive radar and stereo camera in real traffic environments.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Schreier, Matthias and Willert, Volker and Adamy, Jürgen
Title: Grid Mapping in Dynamic Road Environments: Classification of Dynamic Cell Hypothesis via Tracking
Language: English
Abstract:

We propose a method capable of acquiring an occupancy grid map-based representation of the local, static driving environment around an intelligent vehicle in the presence of dynamic objects. These corrupt the representation due to violating the underlying static-world assumptions of common grid mapping algorithms and are therefore detected and filtered from the map. For this purpose, a subsequent step is suggested that identifies, clusters and merges dynamic cell hypothesis in a novel way. Thereafter, an Interacting-Multiple-Model-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) tracker is used to classify of whether cell movements behave consistently with possible movement characteristics of real dynamic objects or are just generated by noise or newly observed static environment. In opposition to many other approaches, the method explicitly combines information of newly occupied and free areas, completes the shape of only partly visible dynamic objects and uses an advanced object tracking scheme to clean the grid from dynamic object corruptions. The method is evaluated with grids generated by an automotive radar and stereo camera in real traffic environments.

Title of Book: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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: IEEE International Conference on Robotics and Automation (ICRA)
Event Location: Hong Kong, China
Event Dates: May 31 – June 7
Date Deposited: 24 Jun 2014 14:00
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