Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

Yang, Junho; Dani, Ashwin P.; Chung, Soon‐Jo; Hutchinson, Seth

doi:10.2514/6.2013-5246

Cited by 8 publications

(6 citation statements)

References 29 publications

(30 reference statements)

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“…Furthermore [75] proposed a vision based SLAM algorithm for an UAV navigating in riverine environments. The suggested algorithm integrated the reflection in the water and developed a reflection matching approach with a robot-centric mapping strategy.…”

Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

View full text Add to dashboard Cite

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“…The observability of the estimation system is substantially improved as a result. Our previous work (Yang, Dani, Chung, & Hutchinson, ) showed preliminary results of the localization and robot‐centric mapping by using reflections. In this work, we analyze the observability of our estimation system under various conditions and validate the effectiveness of our algorithm through numerical simulations and experiments in a larger environment.…”

Section: Related Workmentioning

confidence: 99%

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

Yang

Dani

Chung

et al. 2015

Journal of Field Robotics

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This paper presents a vision-based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three-dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple-view geometry to enhance the observability of the estimation system. We use a robot-centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping. C 2015 Wiley Periodicals, Inc.

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“…An inertial-aided vision-based localization and mapping algorithm is proposed by Yang et al [7]. However, this is specifically developed for a UAV operating in riverine environments.…”

Section: Related Workmentioning

confidence: 99%

Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments

Unicomb

Dantanarayana

Arukgoda

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm.

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Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

Cited by 8 publications

References 29 publications

Survey on Computer Vision for UAVs: Current Developments and Trends

Survey on Computer Vision for UAVs: Current Developments and Trends

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments

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