1,818 research outputs found
Efficient Autonomous Navigation for Planetary Rovers with Limited Resources
Rovers operating on Mars are in need of more and more autonomous features to ful ll their
challenging mission requirements. However, the inherent constraints of space systems make
the implementation of complex algorithms an expensive and difficult task. In this paper
we propose a control architecture for autonomous navigation. Efficient implementations of
autonomous features are built on top of the current ExoMars navigation method, enhancing
the safety and traversing capabilities of the rover. These features allow the rover to detect
and avoid hazards and perform long traverses by following a roughly safe path planned by
operators on ground. The control architecture implementing the proposed navigation mode
has been tested during a field test campaign on a planetary analogue terrain. The experiments
evaluated the proposed approach, autonomously completing two long traverses while
avoiding hazards. The approach only relies on the optical Localization Cameras stereobench,
a sensor that is found in all rovers launched so far, and potentially allows for computationally
inexpensive long-range autonomous navigation in terrains of medium difficulty
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
09111 Abstracts Collection -- Computational Geometry
From March 8 to March 13, 2009, the Dagstuhl Seminar 09111 ``Computational Geometry \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
Reliable outdoor deployment of mobile robots requires the robust
identification of permissible driving routes in a given environment. The
performance of LiDAR and vision-based perception systems deteriorates
significantly if certain environmental factors are present e.g. rain, fog,
darkness. Perception systems based on FMCW scanning radar maintain full
performance regardless of environmental conditions and with a longer range than
alternative sensors. Learning to segment a radar scan based on driveability in
a fully supervised manner is not feasible as labelling each radar scan on a
bin-by-bin basis is both difficult and time-consuming to do by hand. We
therefore weakly supervise the training of the radar-based classifier through
an audio-based classifier that is able to predict the terrain type underneath
the robot. By combining odometry, GPS and the terrain labels from the audio
classifier, we are able to construct a terrain labelled trajectory of the robot
in the environment which is then used to label the radar scans. Using a
curriculum learning procedure, we then train a radar segmentation network to
generalise beyond the initial labelling and to detect all permissible driving
routes in the environment.Comment: accepted for publication at the IEEE Intelligent Transportation
Systems Conference (ITSC) 202
Compact Modeling Technique for Outdoor Navigation
16 pages, 46 figures.In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.This work was supported by the Spanish Government through the MICYT project DPI2003-01170.Publicad
Algorithm for efficient 3D reconstruction of outdoor environments using mobile robots
In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the specified standards. With respect to existing approaches, the proposed approach is an innovation since there are very few information based methods for outdoor reconstruction that use resulting model quality and trajectory cost estimation as criteria for view planning
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
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