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This paper presents a natural corners-based two-dimensional (2D) Simultaneous Localization and Mapping (SLAM) with a robust data association algorithm in a real unknown environment. The corners are extracted from raw laser sensor data and chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are...
This paper proposes the occlusion avoidance method in comers-based simultaneous localization and mapping (SLAM) with different data association algorithms. The redundant or wrong features are extracted if part of the object is occluded. The comers are chosen by intersecting two adjacent line segments and selecting the end-points of some special line segment. When two segments are far enough, the nearest...
This paper presents a 3D point cloud map construction method based on extracted line segments with two mutually-perpendicular laser sensors in unknown indoor environment. To correct the position of mobile robot, the line segments are extracted from the raw sensor data from a horizontally installed laser sensor. In each step, these extracted segments are chosen as landmarks and matched with the stored...
The simultaneous localization and mapping (SLAM) in 2D unknown environment based on the natural corners is presented in this paper. The corners are chosen as landmarks by finding the intersection points or the end points of the line segments extracted from the raw sensor data. The mapping is constructed by the Improved Extended Kalman Filter (IEKF) based SLAM algorithm, the procedure of which is analyzed...
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