Vision-Aided Inertial Navigation for Flight Control

Wu, Allen D.; Johnson, Eric N.; Proctor, Alison A.

doi:10.2514/1.16038

Cited by 102 publications

(34 citation statements)

References 15 publications

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“…The difference between the two last related works, [3] and [17], and the present work is that the last does a fusion with visual sensor only, making possible a minor cost solution, although with less robustness than the others.…”

Section: Related Workmentioning

confidence: 80%

“…Wu, Johson and Proctor [17] present an EKF approach for the vision-aided inertial navigation of an UAV using only an IMU, camera, and magnetometer as sensors for navigation. Using camera images of a target whose position and attitude are given, the vehicle can determine position relative to the target, and therefore derive position in the world.…”

Section: Related Workmentioning

confidence: 99%

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A Visual Data Fusion to Aerial and Land Robots Odometry

Lima

Pio

Carvalho

2013

2013 Latin American Robotics Symposium and Competition

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this paper aims the estimation of the robots motion angles through a linear fusion of two visuals information: Horizon Line position and orientation; and Image Optical Flow. The need of integration of movement estimation techniques is due to the fact that the angles estimation using Horizon information is used with efficiency in several works about aerial robots odometry, but this approach does not enable yaw angle estimation. Optical Flow approach enables the estimation of all of the angles and can be used without the presence of Horizon on the vision field, but can fail in situations which its constraints are not complied. Preliminary experiments made with aerial robots proved the efficiency of the presented method. Despite the difference on the degrees of freedom of other types of robots in comparison with aerial robots, this odometry method can be used on all the types of land robots to estimate its movements. Obviously, in particular cases, the land robot movement can be described with less of the three movement angles because the minor degree of freedom of the robot.

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Section: Related Workmentioning

confidence: 80%

Section: Related Workmentioning

confidence: 99%

A Visual Data Fusion to Aerial and Land Robots Odometry

Lima

Pio

Carvalho

2013

2013 Latin American Robotics Symposium and Competition

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“…Here, the vehicle is flown in urban terrain without narrow or deep canyons where the risk of possible satellite signal dropouts as depicted by Morales and Tsubouchi (2007) is assumed to be low. To compensate for GPS failures and eventually to fly without any satellite navigation, vision-aided flight state estimation based on optical flow is developed (Wu et al 2005). Such optical navigation is not used here, but it is of high importance for future research.…”

Section: Sensor Data Interpretationmentioning

confidence: 99%

Online world modeling and path planning for an unmanned helicopter

Andert

Adolf

2009

Auton Robot

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Mission scenarios beyond line of sight or with limited ground control station access require capabilities for autonomous safe navigation and necessitate a continuous extension of existing and potentially outdated information about obstacles. The presented approach is a novel synthesis of techniques for 3D environment perception and global path planning. A locally bounded sensor fusion approach is used to extract sparse obstacles for global incremental path planning in an anytime fashion. During the flight, a stereo camera checks the field of view along the flight path ahead by analyzing depth images. A 3D occupancy grid is built incrementally. To reduce the high data rate and storage demands of grid-type maps, an approximated polygonal world model is created. For a compacted representation, it uses prisms and ground planes. This enables the system to constantly renew and update its knowledge about obstacles. An incremental heuristic path planner uses both a-priori information as well as incremental obstacle updates to assure a collision-free path at any time. Mapping results from flight tests show the functionality of onboard world modeling from real sensor data. Path planning feasibility is demonstrated within a simulation environment considering world model changes inside the vehicle's field of view.

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“…In recent years, vision sensors have attracted the attention of many researchers as they are a good source of information [3,4,5]. Additionally, related algorithms pertaining to the optic flow and feature point have been the subject of in-depth studies.…”

Section: Related Workmentioning

confidence: 99%

Vision coupled GPS/INS scheme for helicopter navigation

Kim¹,

Lyou

Kwak

2010

J Mech Sci Technol

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This paper presents a framework for a GPS/INS/vision-based helicopter navigation system. The conventional GPS/INS algorithm has weak points such as GPS blockage and jamming, while the helicopter is a speedy and highly dynamic vehicle that may easily lose a GPS signal. A vision sensor is not affected by signal jamming, and the navigation error of such a system does not accumulate. Hence, a GPS/INS/vision-aided navigation scheme was implemented to provide the robust localization suitable for helicopter operations in various environments. The core algorithm is the vision-based SLAM (simultaneous localization and mapping) technique. Flight tests were performed to verify the SLAM-aided vision navigation algorithm. During the tests, it was confirmed that the developed system is sufficiently robust under GPS blockage conditions. The system design, software algorithm, and flight test results are described in this paper.

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Vision-Aided Inertial Navigation for Flight Control

Cited by 102 publications

References 15 publications

A Visual Data Fusion to Aerial and Land Robots Odometry

A Visual Data Fusion to Aerial and Land Robots Odometry

Online world modeling and path planning for an unmanned helicopter

Vision coupled GPS/INS scheme for helicopter navigation

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