3d Structure and Motion Estimation from Range and Intensity Images Using Particle Filtering

This article deals with an estimation method of 3D structure and motion. The object is described by line segments and points assuming that it can be described by a polygonal model. It models the problem which is solved by a nonlinear estimation method: The Particle Filter. This method can account for nonlinear models and non-Gaussian statistics without any linearization stage like the Extended Kalman Filter (EKF), for example. The increase in accuracy is shown on a vision system composed by sensors delivering range and intensity/reflectance image sequences. Finally, the solution is compared with a commonly used state estimation method (EKF).

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