1,378 research outputs found
Visual servoing of a car-like vehicle - an application of omnidirectional vision
In this paper, we develop the switching controller presented by Lee et al. for the pose control of a car-like vehicle, to allow the use of an omnidirectional vision sensor. To this end we incorporate an extension to a hypothesis on the navigation behaviour of the desert ant, cataglyphis bicolor, which leads to a correspondence free landmark based vision technique. The method we present allows positioning to a learnt location based on feature bearing angle and range discrepancies between the robot's current view of the environment, and that at a learnt location. We present simulations and experimental results, the latter obtained using our outdoor mobile platform
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM
This paper describes an image based visual servoing (IBVS) system for a
nonholonomic robot to achieve good trajectory following without real-time robot
pose information and without a known visual map of the environment. We call it
trajectory servoing. The critical component is a feature-based, indirect SLAM
method to provide a pool of available features with estimated depth, so that
they may be propagated forward in time to generate image feature trajectories
for visual servoing. Short and long distance experiments show the benefits of
trajectory servoing for navigating unknown areas without absolute positioning.
Trajectory servoing is shown to be more accurate than pose-based feedback when
both rely on the same underlying SLAM system
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