Dynamic Occupancy Grid Mapping with Recurrent Neural Networks
Schreiber M, Glaeser C, Dietmayer K, Belagiannis V (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2021-May
Pages Range: 6717-6724
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: Xi'an, CHN
ISBN: 9781728190778
DOI: 10.1109/ICRA48506.2021.9561375
Abstract
Modeling and understanding the environment is an essential task for autonomous driving. In addition to the detection of objects, in complex traffic scenarios the motion of other road participants is of special interest. Therefore, we propose to use a recurrent neural network to predict a dynamic occupancy grid map, which divides the vehicle surrounding in cells, each containing the occupancy probability and a velocity estimate. During training, our network is fed with sequences of measurement grid maps, which encode the lidar measurements of a single time step. Due to the combination of convolutional and recurrent layers, our approach is capable to use spatial and temporal information for the robust detection of static and dynamic environment. In order to apply our approach with measurements from a moving ego-vehicle, we propose a method for ego-motion compensation that is applicable in neural network architectures with recurrent layers working on different resolutions. In our evaluations, we compare our approach with a state-of-the-art particle-based algorithm on a large publicly available dataset to demonstrate the improved accuracy of velocity estimates and the more robust separation of the environment in static and dynamic area. Additionally, we show that our proposed method for ego-motion compensation leads to comparable results in scenarios with stationary and with moving ego-vehicle.
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Germany (DE)How to cite
APA:
Schreiber, M., Glaeser, C., Dietmayer, K., & Belagiannis, V. (2021). Dynamic Occupancy Grid Mapping with Recurrent Neural Networks. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 6717-6724). Xi'an, CHN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Schreiber, Marcel, et al. "Dynamic Occupancy Grid Mapping with Recurrent Neural Networks." Proceedings of the 2021 IEEE International Conference on Robotics and Automation, ICRA 2021, Xi'an, CHN Institute of Electrical and Electronics Engineers Inc., 2021. 6717-6724.
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