1,413 research outputs found
Monocular Vision SLAM for Indoor Aerial Vehicles
This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping (SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners
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
From Monocular SLAM to Autonomous Drone Exploration
Micro aerial vehicles (MAVs) are strongly limited in their payload and power
capacity. In order to implement autonomous navigation, algorithms are therefore
desirable that use sensory equipment that is as small, low-weight, and
low-power consuming as possible. In this paper, we propose a method for
autonomous MAV navigation and exploration using a low-cost consumer-grade
quadrocopter equipped with a monocular camera. Our vision-based navigation
system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense
reconstruction of the environment in real-time. Since LSD-SLAM only determines
depth at high gradient pixels, texture-less areas are not directly observed so
that previous exploration methods that assume dense map information cannot
directly be applied. We propose an obstacle mapping and exploration approach
that takes the properties of our semi-dense monocular SLAM system into account.
In experiments, we demonstrate our vision-based autonomous navigation and
exploration system with a Parrot Bebop MAV
Visual SLAM for flying vehicles
The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. © 2008 IEEE
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