519 research outputs found
Performance Evaluation of Vision-Based Algorithms for MAVs
An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Autonomous Flight in Unknown Indoor Environments
http://multi-science.metapress.com/content/80586kml376k2711/This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner. Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.National Science Foundation (U.S.) (NSF Division of Information and Intelligent Systems under grant # 0546467)United States. Army Research Office (ARO MAST CTA)Singapore. Armed Force
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles
In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex,
unknown, unstructured environments, they must be able to navigate with
guaranteed safety, even when faced with a cluttered environment they have no
prior knowledge of. While trajectory optimization-based local planners have
been shown to perform well in these cases, prior work either does not address
how to deal with local minima in the optimization problem, or solves it by
using an optimistic global planner.
We present a conservative trajectory optimization-based local planner,
coupled with a local exploration strategy that selects intermediate goals. We
perform extensive simulations to show that this system performs better than the
standard approach of using an optimistic global planner, and also outperforms
doing a single exploration step when the local planner is stuck. The method is
validated through experiments in a variety of highly cluttered environments
including a dense forest. These experiments show the complete system running in
real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201
Visual 3-D SLAM from UAVs
The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs
confined spaces industrial inspection with micro aerial vehicles and laser range finder localization
This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a mu..
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