Bayesian Bootstrap Filter for integrated GPS and Dead Reckoning Positioning

Khalid, Tuqa; Zribi, Mourad; Jean-Bernard, C.; Mohammed, B.

doi:10.1109/isie.2007.4374828

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Kalman filters (KFs) [14] or particle filters (PFs) [15] are widely used to integrate the orientation and the position. KFs can estimate the states of linear Gaussian state-space models, and PFs can deal with highly nonlinear models.…”

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confidence: 99%

A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

Zhang

2015

IEEE Trans. Ind. Electron.

110

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A more natural means of communicating complex movements to a robot manipulator is where the manipulator copies the movements of human hands. This paper presents a markerless human-robot interface that incorporates Kalman filters (KFs) and particle filters (PFs) to track the posture of human hands. This method allows one operator to control dual robot manipulators by using his/her double hands without any contact devices or markers. The algorithm employs Leap Motion to determine the orientation and the position of the human hands. Although the position and the orientation of the hands can be obtained from the sensor, the measurement errors increase over time due to the noise of the devices and the tracking error. The PFs and KFs are used to estimate the position and the orientation of the human hand. Due to the limitations of the perception and the motor, a human operator cannot accomplish high-precision manipulation without any assistance. An adaptive multispace transformation is employed to assist the operator to improve the accuracy and reliability in determining the posture of the manipulator. The greatest advantage of this method is that the posture of the human hands can be estimated accurately and steadily without any assistant markers. The human-manipulator interface system was experimentally verified in a laboratory, and the results indicate that such an interface can successfully control dual robot manipulators even if the operator is not an expert.Index Terms-Human-robot interface, Kalman filter (KF), markerless, particle filter (PF).

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confidence: 99%

A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

Zhang

2015

IEEE Trans. Ind. Electron.

110

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“…In the whole process of our virtual acupuncture system, firstly, by five Leap Motions, we get the position as well as orientation of the hand. Because the error will exist and accumulate over time during measuring the data of the hand, to solve this problem, the Kalman Filter [4] algorithm and the Particle Filter [5] algorithm which can estimate the position and the orientation of the hand respectively are introduced. Then the 3D Max can be used to build the joint model of the hand.…”

Section: Overview Figure I Virtual Acupuncture Systemmentioning

confidence: 99%

The Virtual Acupuncture Based on Natural Gesture and Force Feedback

Lin¹,

Du²,

Zhang³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-the paper presents a virtual acupuncture system which uses digital virtual human model to carry out the virtual acupuncture experiments by using natural gesture and force feedback. In the paper, five Leap Motions are employed to measure the position and orientation of the operator's hand. Due to the tracking errors and the noise of equipment, the measurement errors will increase over time. So, Kalman Filter and Particle Filter are introduced to estimate the position and orientation of the hand. Then we use 3D Max to build the joint model and OSG (OpenSceneGraph) to refactor the model of the hand. Because it is needed to detect the collision of two models including hand model and human model, here the K-DOPs collision detection algorithm is used to achieve it. When the hand model and the human model collide, we need to use noncontact force feedback which using Lorentz Force to let the operator experience the drag force like it comes from the human model through simply wearing a small magnet. Therefore, the virtual Acupuncture experiment can be carried out successfully on the digital virtual human model.

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“…The hand tracking system consists of five Leap Motions, it can obtain the position date and the orientation date of gestures. Also, Kalman Filter [3] and Particle Filter [4] are employed to reduce the data error. And then we can apply the noncontact force feedback system with four coils using Lorenz force to feel the drag force like in a real environment.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the orientation and the position of gestures will be collected by the hand tracking system using five Leap Motions. Then we use the Kalman Filter [3] algorithm and the Particle Filter [4] algorithm to evaluate the orientation and position of our hands. After the data of our hands fused, we can use 3D Max to build the joint model of our hands, and then we can get an accurate three-dimension model of our hands.…”

Section: Introductionmentioning

confidence: 99%

The Virtual Assembly Technology Based on Natural Gesture

Chen

Du²,

Zhang³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-the purpose of this paper is to present a method called virtual assembly technology based on natural gesture. In the proposed method, five Leap Motions are fixed on the table to measure the position of the hands. Due to the tracking errors and the noise of equipment, the measurement errors will increase over time. Therefore, we use Kalman Filter and Particle Filter to evaluate the position and orientation of our hands. Then we use 3D Max to build the joint model of our hands, and OSG (OpenSceneGraph) to refactor the model of the hand. We use k-DOPs collision detection algorithm to detect collisions between hands model and human model. Then we use noncontact force feedback to make the operator experience the drag force like in a real environment, so that the operator can use natural gesture to do various assembly operation on the parts of the products in an interactive virtual assembly environment.

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Bayesian Bootstrap Filter for integrated GPS and Dead Reckoning Positioning

Cited by 8 publications

References 6 publications

A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

The Virtual Acupuncture Based on Natural Gesture and Force Feedback

The Virtual Assembly Technology Based on Natural Gesture

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