A Kalman filter approach for accurate 3D motion estimation from a sequence of stereo images

Lee, S.; Kay, Y.

doi:10.1109/icpr.1990.118073

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…Quaternion is equivalent to Euler angles functionally, but more convenient to be utilized for iterative computation of parameters because it doesn't include trigonometric functions such as sine and cosine. [12] presented a quaternionbased Kalman filter approach for accurate 3D motion estimation, which overcame the accuracy degradation caused by noisy images, and ultimate rotational parameters were computed from quaternions by iterated least squares method. In fact, considerable noise may lead to the nonlinearity of filters and various nonlinear filtering methods are adopted.…”

Section: Parameter Estimation Methodsmentioning

confidence: 99%

A Review of 3D Pose Estimation from a Monocular Image Sequence

Shan¹,

Ji²,

Zhou³

2009

2009 2nd International Congress on Image and Signal Processing

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3D pose estimation is an important part of 3D motion estimation, which is useful for visualized operation and control. The paper classified approaches of 3D pose estimation from a monocular image sequence into three types: feature-based approach, optical-flow-based approach and model-based approach. In feature-based approaches, 3D motion are estimated by observing a set of 2D features, such as points, lines, junctions and corners, over two or more images. On the contrary, opticalflow-based approaches estimate instantaneous 3D motion from image plane velocity. Both of them can complete the estimation without any a priori knowledge of the object, but the computation is complex and time-consuming. Mode-based approach avoids the burden of computation, but relies on the a priori knowledge of the object. Current research status of each approach is reviewed. Comparison of these three approaches and developing trends of 3D pose estimation are discussed in the conclusion.

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Section: Parameter Estimation Methodsmentioning

confidence: 99%

A Review of 3D Pose Estimation from a Monocular Image Sequence

Shan¹,

Ji²,

Zhou³

2009

2009 2nd International Congress on Image and Signal Processing

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“…The most important advantage of the hardware implementation is the possibility of computation while imaging. Using the hardware at maximum performance shortest system response times for calculating the depth map can be reached The data is then transmitted to the ARM9 processor for further complex processing steps such as clustering (section 3) and Kalman filters [5] for tracking. Furthermore section 5 describes a solution of calculation the 3-D-points with simultaneous correction of systematic errors capable for embedded software.…”

Section: Hardware-software Co-designmentioning

confidence: 99%

Real-time, 3-D multi object position estimation and tracking

Kaszubiak

Tornow

Kuhn

et al. 2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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For autonomously acting robots and driver assistance systems powerful optical stereo sensor systems are required. Object positions and environmental conditions have to be acquired in real-time. In this paper a hardware-software co-design is applied, acting within the presented stereophotogrammetric system. For calculation of the depth map an optimized algorithm is implemented as a hierarchical parallel hardware solution. By adapting the image resolution to the distance, real-time processing is possible. The object clustering and the tracking is realized in a processor. The density distribution of the disparity in the depth map (disparity histogram) is used for object detection. A Kalman filter stabilizes the parameters of the results.

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“…The structure parameters are then used for motion estimation. Lee and Kay [7] employ a Kalman filter to smooth the errors resulted from least-squares motion estimation and to estimate the object's orientation. Cui et al [8] describe a recursive-batch nonlinear optimization approach to obtain optimal motion and structure estimation of a rigid scene from a long sequence of monocular images.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Model for Optimizing Sensor Pose in Object Motion Estimation Applications

Tsui¹,

Basir²

2006

Intelligent Automation & Soft Computing

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An evolutionary control paradigm for vision-system pose planning for object motion estimation is proposed. The control of the vision system is embedded in the motion estimation process so as to adapt to the dynamic object motion behavior. A Kalman filter is employed as the motion estimator. In the Kalman filter formulation, a noise influence matrix is introduced to model the influence of vision system parameters on the measurement uncertainties. The estimation uncertainties in the Kalman filter formulation are represented in the form of a Riccati equation. This equation describes the estimation uncertainties as an evolution process that is controlled by the vision system parameters. The control task is formulated as an optimization problem. A novel transformation of the vision system parameters is developed to relax the computational complexity of the optimization process. A hybrid genetic algorithm is proposed to search for the optimal vision system pose that is occlusion free. A series of experiments are conducted to evaluate the performance of the proposed object motion estimation model.

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A Kalman filter approach for accurate 3D motion estimation from a sequence of stereo images

Cited by 9 publications

References 5 publications

A Review of 3D Pose Estimation from a Monocular Image Sequence

A Review of 3D Pose Estimation from a Monocular Image Sequence

Real-time, 3-D multi object position estimation and tracking

An Evolutionary Model for Optimizing Sensor Pose in Object Motion Estimation Applications

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