7,148 research outputs found
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots
This paper shows and evaluates a novel approach to integrate a non-invasive
Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to
mentally drive a telepresence robot. Controlling a mobile device by using human
brain signals might improve the quality of life of people suffering from severe
physical disabilities or elderly people who cannot move anymore. Thus, the BCI
user is able to actively interact with relatives and friends located in
different rooms thanks to a video streaming connection to the robot. To
facilitate the control of the robot via BCI, we explore new ROS-based
algorithms for navigation and obstacle avoidance, making the system safer and
more reliable. In this regard, the robot can exploit two maps of the
environment, one for localization and one for navigation, and both can be used
also by the BCI user to watch the position of the robot while it is moving. As
demonstrated by the experimental results, the user's cognitive workload is
reduced, decreasing the number of commands necessary to complete the task and
helping him/her to keep attention for longer periods of time.Comment: Accepted in the Proceedings of the 2018 IEEE International Conference
on Robotics and Automatio
Navite: A Neural Network System For Sensory-Based Robot Navigation
A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.Defense Advanced Research Projects Agency (AFOSR 90-0083); Office of Naval Research (N00014-92-J-l309); Consejo Nacional de Ciencia y TecnologĂa (63l462
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