Pose Estimation of UAVs Using Stereovision
22 Dec 2019-pp 185-197
TL;DR: In this article, the authors proposed an architecture for estimating the pose of an UAV using computer vision based methods, which consists of three sub modules, namely, Image Segmentation Block (IS), Perspective Transform (PT) and Pose Determination (PD) respectively.
Abstract: Pose of an UAV has been traditionally estimated by On Board Computers (OBCs) using Inertial Measurement Unit (IMU) sensor data as input. In this paper, the development of an architecture for estimating the pose of an UAV using popular Computer Vision based methods has bis proposed. Which consists of three sub modules, namely, Image Segmentation Block (IS), Perspective Transform (PT) and Pose Determination (PD) respectively. IS block uses segmentation to detect salient points from the image where as the PT block transforms image coordinates to world coordinates, finally the PD block uses camera parameters and object dimensions to provide Attitude and Translation matrix of the UAV. The proposed approach can adjust with change in environmental parameters. The system was characterized by observing the error in the estimated yaw and estimated depth. Analysis was made on the nature and variation of error with various experimental parameters. An in depth analysis of the paper was carried out. We were finally able to devise an algorithm for estimating the pose of a body without establishing any communication with the body.
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TL;DR: The skeleton of this framework is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration.
Abstract: Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (a skeleton). The skeleton is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing large-scale loop closure and precise localization in real time.
624 citations
TL;DR: A body of work aimed at extending the reach of mobile navigation and mapping is described, showing how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system.
Abstract: In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.
154 citations
03 May 2010
TL;DR: The proposed algorithm robustly estimates the helicopter's 3D position with respect to a reference landmark, with a high quality on the position and orientation estimation when the aircraft is flying at low altitudes, a situation in which the GPS information is often inaccurate.
Abstract: This article presents a real time Unmanned Aerial Vehicles UAVs 3D pose estimation method using planar object tracking, in order to be used on the control system of a UAV. The method explodes the rich information obtained by a projective transformation of planar objects on a calibrated camera. The algorithm obtains the metric and projective components of a reference object (landmark or helipad) with respect to the UAV camera coordinate system, using a robust real time object tracking based on homographies. The algorithm is validated on real flights that compare the estimated data against that obtained by the inertial measurement unit IMU, showing that the proposed method robustly estimates the helicopter's 3D position with respect to a reference landmark, with a high quality on the position and orientation estimation when the aircraft is flying at low altitudes, a situation in which the GPS information is often inaccurate. The obtained results indicate that the proposed algorithm is suitable for complex control tasks, such as autonomous landing, accurate low altitude positioning and dropping of payloads.
81 citations
TL;DR: In this paper, a vision-based method is proposed to assist landing of a UAV, which only makes use of the two edge lines and the threshold line on the runway, and the position vector of the UAV can be calculated using the coplanarity characteristic.
Abstract: In order to assist landing of Unmanned Aircraft Vehicle (UAV), a vision-based method is proposed, which only makes use of the two edge lines and the threshold line on the runway. In the earlier stage, through seeking the geometric constraints among the three lines, the coordinates of the points of intersection of the three lines in the camera frame are solved, and the vectors of the three lines in the camera frame can be obtained using the vanishing point. The attitudes are solved by the Umeyama method, and the position vector of the UAV can be calculated using the coplanarity characteristic. In the later stage, the threshold line is invisible, the yaw, the pitch, the cross position and the altitude can be estimated by two edge lines. Simulation results show the proposed algorithm is accurate and fast.
11 citations
01 Dec 2017
TL;DR: The method can be applied to assist the existing IR-marker-based pose estimation system to locate the target when there are not enough IR markers to be detected and perform as a possible solution for multi-robot localization.
Abstract: In this paper, we propose a system for UAV (Unmanned Aerial Vehicle) 6D pose estimation. The proposed system is experimentally evaluated and verified in a simulated environment. A pair of infra-red active markers and a colored passive marker are utilized to mark the relative pose of the target. Accordingly, two different types of sensors, including the IR (infra-red) and color camera are applied. A PSO (Particle Swarm Optimization) algorithm on the iteration-varying weight is employed to solve the optimal pose of the UAV. The method can be applied to assist the existing IR-marker-based pose estimation system to locate the target when there are not enough IR markers to be detected. In addition, this system is also able to perform as a possible solution for multi-robot localization by identifying the passive markers with different colors on different targets.
10 citations