3,056 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
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
CORBYS cognitive control architecture for robotic follower
In this paper the novel generic cognitive robot control architecture CORBYS is presented. The objective of the CORBYS architecture is the integration of high-level cognitive modules to support robot functioning in dynamic environments including interacting with humans. This paper presents the preliminary integration of the CORBYS architecture to support a robotic follower. Experimental results on high-level empowerment-based trajectory planning have demonstrated the effectiveness of ROS-based communication between distributed modules developed in a multi-site research environment as typical for distributed collaborative projects such as CORBYS
A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision
Advanced driving assistance systems (ADAS) form a complex multidisciplinary research field, aimed at improving traffic efficiency and safety. A realistic analysis of the requirements and of the possibilities of the traffic environment leads to the establishment of several goals for traffic assistance, to be implemented in the near future (ADASE, INVENT
Low computational-cost detection and tracking of dynamic obstacles for mobile robots with RGB-D cameras
Deploying autonomous robots in crowded indoor environments usually requires
them to have accurate dynamic obstacle perception. Although plenty of previous
works in the autonomous driving field have investigated the 3D object detection
problem, the usage of dense point clouds from a heavy LiDAR and their high
computation cost for learning-based data processing make those methods not
applicable to small robots, such as vision-based UAVs with small onboard
computers. To address this issue, we propose a lightweight 3D dynamic obstacle
detection and tracking (DODT) method based on an RGB-D camera, which is
designed for low-power robots with limited computing power. Our method adopts a
novel ensemble detection strategy, combining multiple computationally efficient
but low-accuracy detectors to achieve real-time high-accuracy obstacle
detection. Besides, we introduce a new feature-based data association method to
prevent mismatches and use the Kalman filter with the constant acceleration
model to track detected obstacles. In addition, our system includes an optional
and auxiliary learning-based module to enhance the obstacle detection range and
dynamic obstacle identification. The users can determine whether or not to run
this module based on the available computation resources. The proposed method
is implemented in a small quadcopter, and the experiments prove that the
algorithm can make the robot detect dynamic obstacles and navigate dynamic
environments safely.Comment: 8 pages, 12 figures, 2 table
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