1,496 research outputs found
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of
intelligent vehicles. A major challenge still to overcome is the prediction of
complex downtown scenarios with multiple road users, e.g., pedestrians, bikes,
and motor vehicles, interacting with each other. This contribution tackles this
challenge by combining a Bayesian filtering technique for environment
representation, and machine learning as long-term predictor. More specifically,
a dynamic occupancy grid map is utilized as input to a deep convolutional
neural network. This yields the advantage of using spatially distributed
velocity estimates from a single time step for prediction, rather than a raw
data sequence, alleviating common problems dealing with input time series of
multiple sensors. Furthermore, convolutional neural networks have the inherent
characteristic of using context information, enabling the implicit modeling of
road user interaction. Pixel-wise balancing is applied in the loss function
counteracting the extreme imbalance between static and dynamic cells. One of
the major advantages is the unsupervised learning character due to fully
automatic label generation. The presented algorithm is trained and evaluated on
multiple hours of recorded sensor data and compared to Monte-Carlo simulation
RH-Map: Online Map Construction Framework of Dynamic Objects Removal Based on Region-wise Hash Map Structure
Mobile robots navigating in outdoor environments frequently encounter the
issue of undesired traces left by dynamic objects and manifested as obstacles
on map, impeding robots from achieving accurate localization and effective
navigation. To tackle the problem, a novel map construction framework based on
3D region-wise hash map structure (RH-Map) is proposed, consisting of front-end
scan fresher and back-end removal modules, which realizes real-time map
construction and online dynamic object removal (DOR). First, a two-layer 3D
region-wise hash map structure of map management is proposed for effective
online DOR. Then, in scan fresher, region-wise ground plane estimation (R-GPE)
is adopted for estimating and preserving ground information and Scan-to-Map
Removal (S2M-R) is proposed to discriminate and remove dynamic regions.
Moreover, the lightweight back-end removal module maintaining keyframes is
proposed for further DOR. As experimentally verified on SemanticKITTI, our
proposed framework yields promising performance on online DOR of map
construction compared with the state-of-the-art methods. And we also validate
the proposed framework in real-world environments
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
- …