Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks

Schreiber M, Belagiannis V, Glaser C, Dietmayer K (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Proceedings - IEEE International Conference on Robotics and Automation Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 8587-8593

Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation

Event location: Paris, FRA

ISBN: 9781728173955

DOI: 10.1109/ICRA40945.2020.9196702

Abstract

In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy prob-ability and the two dimensional velocity. As input data, our approach relies on measurement grid maps, which contain occupancy probabilities, generated with lidar measurements. Given this configuration, we propose a recurrent neural net-work architecture to predict a dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell, by using a sequence of measurement grid maps. Our network architecture contains convolutional long-short term memories in order to sequentially process the input, makes use of spatial context, and captures motion. In the evaluation, we quantify improvements in estimating the velocity of braking and turning vehicles compared to the state-of-the-art. Additionally, we demonstrate that our approach provides more consistent velocity estimates for dynamic objects, as well as, less erroneous velocity estimates in static area.

Authors with CRIS profile

Vasileios Belagiannis

Involved external institutions

Universität Ulm DE Germany (DE) Robert Bosch GmbH DE Germany (DE)

How to cite

APA:

Schreiber, M., Belagiannis, V., Glaser, C., & Dietmayer, K. (2020). Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 8587-8593). Paris, FRA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Schreiber, Marcel, et al. "Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks." Proceedings of the 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, FRA Institute of Electrical and Electronics Engineers Inc., 2020. 8587-8593.

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