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New methods based on vision have emerged in the area of mobile vehicle localization. Such methods offer an improved alternative in terms of accuracy to traditional localization methods like wheel odometry. In this paper we propose such a method that does not compromise precision and can run in real time. Depending on environment, feature numbers are(More)
This paper proposes an accurate localization method, which consists of a non-probabilistic motion model and Generalized Iterative Closet Point (GICP). The most encountered problem of using motion models is to determine empirical parameters, which represent the systemic errors and the non-systemic errors. The perfect representation of those errors is an(More)
The fundamental problem of localization remains one of the key areas of improvement concerning any type of mobile vehicle. Lately great interest in the research community was presented for vision based localization methods. In this article a solution is proposed for radically improving precision when a stereo camera is used for image acquisition. While(More)
This paper presents a new type of SLAM for indoor and outdoor environments. This method solves only the simplest localization problem, position tracking, and does not use the mapping information in localization. The proposed SLAM uses Hybrid Odometry for the localization. Mapping is based on LDPDs (Local differential Probability Distances) and accumulated(More)
This paper proposes the Lightweight Visual Odometry algorithm for mobile robot localization. The proposed algorithm deals with two main difficulties: real-time functionality and robustness against environmental noise. In order to obtain real-time computation we use a closed form solution that approximates the transformation between successive pairs of(More)
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