Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

Abeywardena, Dinuka; Huang, Shoudong; Barnes, Ben; Dissanayake, Gamini; Kodagoda, Sarath

doi:10.1109/icra.2016.7487290

Cited by 19 publications

(8 citation statements)

References 24 publications

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“…The method can solve the fast-motion problem, but does not solve the low-texture issue. Another group generated an IMU-aided VO system 14 on aerial vehicles (AVs) that considers the dynamic model of AVs to deal with drift-free velocity and altitude estimations. The IMU and VO were not based on mapping and localization, but almost all such work in the AV (a quad-rotor robot, in that case) focused on the weight of the system and, as a result, the velocity estimation.…”

Section: Related Workmentioning

confidence: 99%

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

et al. 2021

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SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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Section: Related Workmentioning

confidence: 99%

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

et al. 2021

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“…7 (i)) that could hover stably at various pitch angles with four tiltable propellers. Abeywardena et al [94] present a quadcopter (Fig. 7 (j)) that uses an extended Kalman filter (EKF) to produce high-frequency odometry by fusing information from an IMU sensor and a monocular camera.…”

Section: A Quadcoptersmentioning

confidence: 99%

Recent Developments in Aerial Robotics: A Survey and Prototypes Overview

Liew,

DeLatte,

Takeishi

et al. 2017

Preprint

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In recent years, research and development in aerial robotics (i.e., unmanned aerial vehicles, UAVs) has been growing at an unprecedented speed, and there is a need to summarize the background, latest developments, and trends of UAV research. Along with a general overview on the definition, types, categories, and topics of UAV, this work describes a systematic way to identify 1,318 high-quality UAV papers from more than thirty thousand that have been appeared in the top journals and conferences. On top of that, we provide a bird's-eye view of UAV research since 2001 by summarizing various statistical information, such as the year, type, and topic distribution of the UAV papers. We make our survey list public and believe that the list can not only help researchers identify, study, and compare their work, but is also useful for understanding research trends in the field. From our survey results, we find there are many types of UAV, and to the best of our knowledge, no literature has attempted to summarize all types in one place. With our survey list, we explain the types within our survey and outline the recent progress of each. We believe this summary can enhance readers' understanding on the UAVs and inspire researchers to propose new methods and new applications.Index Terms-Unmanned aerial vehicle (UAV), unmanned aircraft system (UAS), micro aerial vehicle (MAV), aerial robotics, flying robots, drone, vertical takeoff and landing aircraft (VTOL). I. INTRODUCTIONU NMANNED aerial vehicle (UAV) research and development has been growing rapidly over the past decade. In academia, there are more than 60 UAV papers in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and IEEE International Conference on Robotics and Automation (ICRA) in 2016 alone. In the commercial sector, the annual Aerospace Forecast Report [1] released by the United States Federal Aviation Administration (FAA) estimates that more than seven million UAVs will be purchased by 2020. Another recent report [2] released by Pricewaterhouse-Coopers (PwC)-the second largest professional services firm in the world-estimates the global market for applications of UAVs at over $127 billion in 2020.Despite rapid growth, there is no survey paper that summarizes the background, latest developments, and trends of the UAV research. And, because the number of UAV papers has grown rapidly in recent years, researchers often find it hard to

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“…Aerial photography including visual inspection is one of the main applications of the UAV (Bonnin‐Pascual, Ortiz, Garcia‐Fidalgo, & Company, ; Morgenthal & Hallermann, ; Omari, Gohl, Burri, Achtelik, & Siegwart, ). Visual servo using visual SLAM or odometry with onboard cameras are active topic of research on UAV (Abeywardena, Huang, Barnes, Dissanayake, & Kodagoda, ; Meier, Brockers, Matthies, Siegwart, & Weiss, ; Nikolic et al, ; Piasco, Marzat, & Sanfourche, ). The positions where a camera is equipped at are under, on or by the body of UAV.…”

Section: Introductionmentioning

confidence: 99%

Stable camera position control of unmanned aerial vehicle with three‐degree‐of‐freedom manipulator for visual test of bridge inspection

Ikeda

Minamiyama

Yasui

et al. 2019

Journal of Field Robotics

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This paper describes a camera position control with aerial manipulator for visual test of bridge inspection. Our developed unmanned aerial vehicle (UAV) has three-degree-offreedom (3-DoF) manipulator on its top to execute visual or hammering test of the inspection. This paper focuses on the visual test. A camera was implemented at the end of the manipulator to acquire images of the narrow space of the bridge such as bearings, which the conventional UAV without the camera-attached manipulators at its top cannot achieve the fine visual test. For the visual test, it is desirable that the camera is above the body with enough distance between the camera and the body. As obvious, the camera position in the inertial coordinate system is effected by the movement of the body.Therefore we implement the camera position control compensating the body movement into the UAV. As a result of an experiment, it is assessed that the proposed control reduces the position error of the camera comparing the one of the body. The mean position error of the camera is 0.039 m that is 51.4% of the one of the body. Our world-first study enables to acquire the image of the bearing of the bridge by a camera mounted at the end effector of aerial manipulator fixed on UAV.

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Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

Cited by 19 publications

References 24 publications

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Recent Developments in Aerial Robotics: A Survey and Prototypes Overview

Stable camera position control of unmanned aerial vehicle with three‐degree‐of‐freedom manipulator for visual test of bridge inspection

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