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This paper presents a method of 3D localization using image edge-points detected from binocular stereo image sequences. The proposed method calculates camera poses using visual odometry, and updates the poses by reducing the accumulated errors using landmark recognition. Landmark recognition is done based on robust and scalable image-retrieval using image edge-points with SIFT descriptors and a vocabulary...
Robot localization and mapping is an important function of determining the robot pose in an unknown environment. This paper studies the pose estimation by integrating the sensing information both from proprioceptive and exteroceptive sensors. Specifically, the iterative closest point (ICP) algorithm is utilized and modified for characterizing the feature matching of environmental information obtained...
The high performance mobile robots nowadays should operate themselves in many applications. One attractive task is to build the map, meanwhile estimate their poses for the locomotion in real time. The simultaneous localization and mapping (SLAM) technique is once technique which can solve this demand. The selecting sensors are essential factors for generation the high accuracy output. This paper present,...
This paper describes a new solution of the simultaneous localization and mapping (SLAM) problem. Instead of building one global consistent map, aimed by the most common SLAM techniques, we compute a set of local metrical maps and fuse them to a graph-like structure resulting in a topological map. Thus, our approach does not require a global metrical map consistency. The main contribution of this paper...
In a multi-robot system, in which each of the robots constructs its own local map, it is necessary to perform the fusion of these maps into a global one. This task is normally performed in two different steps: by aligning the maps and then merging the data. This paper focusses on the first step: Map Alignment, which consists in obtaining the transformation between the local maps built independently...
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