Pose estimation using line-based dynamic vision and inertial sensors

Rehbinder, H.; Ghosh, Bijoy K.

doi:10.1109/tac.2002.808464

Cited by 169 publications

(104 citation statements)

References 30 publications

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“…The approach presented in this paper does not address the problem of estimating the entire rotation matrix R. To be able to do this, some additional sensor has to be used. The use of vision together with rate gyros has been studied by Rehbinder and Ghosh (2003).…”

Section: Summary and Discussionmentioning

confidence: 99%

Drift-free attitude estimation for accelerated rigid bodies

Rehbinder

2004

Automatica

196

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In this paper we study the attitude estimation problem for an accelerated rigid body using gyros and accelerometers. The application in mind is that of a walking robot and particular attention is paid to the large and abrupt changes in accelerations that can be expected in such an environment. We propose a state estimation algorithm that fuses data from rate gyros and accelerometers to give long-term drift free attitude estimates. The algorithm does not use any local parameterization of the rigid body kinematics and can thus be used for a rigid body performing any kind of rotations. The algorithm is a combination of two non-standard, but in a sense linear, Kalman ÿlters between which a trigger based switching takes place. The kinematics representation used makes it possible to construct a linear algorithm that can be shown to give convergent estimates for this nonlinear problem. The state estimator is evaluated in simulations demonstrating how the estimates are long-term stable even in the presence of gyro drift. ?

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Section: Summary and Discussionmentioning

confidence: 99%

Drift-free attitude estimation for accelerated rigid bodies

Rehbinder

2004

Automatica

196

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“…For example, rigid body pose estimation using inertial sensors and a monocular camera is considered in Ref. 13, and it is shown how rotation estimation can be decoupled from position estimation. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors

Sun

Jia

2016

JRNAL

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This paper is concerned with the attitude/position estimation of a rigid-body using inertial and vision sensors. By employing the Newton-Euler method, a kinematic model is developed for the rigid-body by treating the inertial measurements as inputs. Based on the coordinate transformation, a nonlinear visual observation model is proposed by using the image coordinates of feature points as observations. Then the Extended Kalman filter (EKF) is utilized to estimate the attitude/position recursively. The effectiveness of the proposed algorithm is evaluated by simulation.

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“…In the monocular case, without any information about the scene, the camera translation can only be estimated up to a scale factor [2]. Combining the output of the camera with inertial sensors can give additionnal information and make the problem observable [11,8]. One application of this type of data fusion is the field of inertial navigation.…”

Section: Introductionmentioning

confidence: 99%

“…The main body of research has been devoted to feature-based methods. They assume the point correspondences in all the images of the sequence are available (or line correspondances [11] etc.). They require to select good features in the image.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of inertial and visual: a geometrical observer-based approach

Bonnabel¹,

Rouchon²,

Béji³

et al. 2009

AIP Conference Proceedings

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The problem of combination between inertial sensors and CCD cameras is of paramount importance in various applications in robotics and autonomous navigation. In this paper we develop a totally geometric model for analysis of this problem, independently from a camera model and from the structure of the scene (landmarks etc.). This formulation can be used for data fusion in several inertial navigation problems. The estimation is then decoupled from the structure of the scene. We use it in the particular case of the estimation of the gyroscopes bias and we build a nonlinear observer which is easy to compute, provides an estimation of the biais, filters the image, and is by construction very robust to noise.

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Pose estimation using line-based dynamic vision and inertial sensors

Cited by 169 publications

References 30 publications

Drift-free attitude estimation for accelerated rigid bodies

Drift-free attitude estimation for accelerated rigid bodies

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors

Fusion of inertial and visual: a geometrical observer-based approach

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