851 research outputs found
Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments
This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version
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
Review and classification of vision-based localisation techniques in unknown environments
International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
SPINS: Structure Priors aided Inertial Navigation System
Although Simultaneous Localization and Mapping (SLAM) has been an active
research topic for decades, current state-of-the-art methods still suffer from
instability or inaccuracy due to feature insufficiency or its inherent
estimation drift, in many civilian environments. To resolve these issues, we
propose a navigation system combing the SLAM and prior-map-based localization.
Specifically, we consider additional integration of line and plane features,
which are ubiquitous and more structurally salient in civilian environments,
into the SLAM to ensure feature sufficiency and localization robustness. More
importantly, we incorporate general prior map information into the SLAM to
restrain its drift and improve the accuracy. To avoid rigorous association
between prior information and local observations, we parameterize the prior
knowledge as low dimensional structural priors defined as relative
distances/angles between different geometric primitives. The localization is
formulated as a graph-based optimization problem that contains
sliding-window-based variables and factors, including IMU, heterogeneous
features, and structure priors. We also derive the analytical expressions of
Jacobians of different factors to avoid the automatic differentiation overhead.
To further alleviate the computation burden of incorporating structural prior
factors, a selection mechanism is adopted based on the so-called information
gain to incorporate only the most effective structure priors in the graph
optimization. Finally, the proposed framework is extensively tested on
synthetic data, public datasets, and, more importantly, on the real UAV flight
data obtained from a building inspection task. The results show that the
proposed scheme can effectively improve the accuracy and robustness of
localization for autonomous robots in civilian applications.Comment: 14 pages, 14 figure
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