4 research outputs found
Radar-based Dynamic Occupancy Grid Mapping and Object Detection
Environment modeling utilizing sensor data fusion and object tracking is
crucial for safe automated driving. In recent years, the classical occupancy
grid map approach, which assumes a static environment, has been extended to
dynamic occupancy grid maps, which maintain the possibility of a low-level data
fusion while also estimating the position and velocity distribution of the
dynamic local environment. This paper presents the further development of a
previous approach. To the best of the author's knowledge, there is no
publication about dynamic occupancy grid mapping with subsequent analysis based
only on radar data. Therefore in this work, the data of multiple radar sensors
are fused, and a grid-based object tracking and mapping method is applied.
Subsequently, the clustering of dynamic areas provides high-level object
information. For comparison, also a lidar-based method is developed. The
approach is evaluated qualitatively and quantitatively with real-world data
from a moving vehicle in urban environments. The evaluation illustrates the
advantages of the radar-based dynamic occupancy grid map, considering different
comparison metrics.Comment: Accepted to be published as part of the 23rd IEEE International
Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece,
September 20-23, 202
Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks
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 probability 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 network 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.Comment: Accepted for presentation at the 2020 International Conference on
Robotics and Automation (ICRA), May 31 - June 4, 2020, Paris, Franc