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In this work we propose the use of machine learning techniques to improve Simultaneous Localization and Mapping (SLAM) using an extended Kalman filter (EKF) and visual information for robot navigation. We are using the Viola and Jones approach for looking specific visual landmarks in environment. The landmarks are used to improve the robot localization in the EKF-SLAM system. Our experiments validate...
We propose a structure and motion estimation scheme based on a dynamic systems approach, where states and parameters in a perspective system are estimated. An online method for structure and motion estimation in densely sampled image sequences is presented. The proposed method is based on an extended Kalman filter and a novel parametrization. We derive a dynamic system describing the motion of the...
A modified covariance extended Kalman filter (MVEKF) algorithm is proposed to the monocular simultaneous localization and mapping (SLAM) in this paper. Recent literatures have shown that it is possible to solve the monocular SLAM using the Extended Kalman Filter (EKF) and the inverse-depth parameterization. However, the EKF algorithm has its intrinsic disadvantage such as the divergence. Here we propose...
Autonomous and safe robot navigation requires the capability to simultaneously building a map of the environment and a selflocalization of the robot itself. This is known as the SLAM (Simultaneous Localization and Mapping) problem. In such a context, omnidirectional camera looks like a very interesting sensor since it allows a full 360 degrees field of vision. Complexity of the SLAM methods dramatically...
This paper presents a method for camera pose tracking that uses a partial knowledge about the scene. The method is based on monocular vision simultaneous localization and mapping (SLAM). With respect to classical SLAM implementations, this approach uses previously known information about the environment (rough map of the walls) and profits from the various available databases and blueprints to constraint...
In visual SLAM features are extracted from images as landmarks. Features should be easily and fast extracted and should be reliably matched in different situations. However, most point features extraction methods used in visual SLAM cannot trade off the reliability of matching and the speed of extraction. In our monocular vision SLAM research, a new feature extractor SURF, which provides both robust...
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