무인이동로봇의 3차원 위치 추정

3차원 비전 기술을 이용한 무인이동로봇의 위치인식기술

3차원 비전 기술을 이용한 무인이동로봇의 위치인식(Localization)기술을 연구하고 있습니다. 로봇이 스스로 주행하기 위해서는 자신의 위치를 정확히 추정할 필요가 있습니다. 본 연구에서는 360도 레이저 거리정보와 3차원 지형정보 (DSM)을 등록하여 로봇의 위치를 추정합니다. 로봇의 주행 속도를 높이기 위하여 3차원 위치추정을 실시간으로 구현하고 있습니다.

SLAM based on 2D Range Scan Matching

SLAM (Simultaneous Localization and Mapping) is a technique

2DScan

 

 


SLAM (Simultaneous Localization and Mapping) is a technique used by a mobile robot to build a map within an unknown environment and at the same time compute its current location. Our research is based on matching two consecutive 2D range scans which are obtained from a LRF (Laser Range Finder) mounted on a mobile robot.


The most widely used algorithm for matching range scans is the Iterative Closest Points (ICP) algorithm. This algorithm is based on an iterative process in two steps: first a set of corresponding pairs between two consecutive scans is selected, and second the error between corresponding pairs is minimized to find the rigid-body transformation between two scans. This process is repeated until two scans are converged. The most critical part of the ICP is the establishment of corresponding point-pairs. For any point in the first scan, the conventional ICP algorithm finds the closest point in the second scan (the nearest neighbor) its correspondence.

 


While ICP has advantages of easy implementation, it has many drawbacks because of the non-linear characteristic. ICP can fall into a bad local minimum due to outliers, occlusions and poor initial alignment. Our research interest is to increase the performance of the conventional ICP and adapt it to the real-time SLAM. In order to speed up the convergence, the point-to-line metric is adopted. The point-to-line metric has quadratic convergence instead of linear convergence of the conventional point-to-point metric, as shown in the next figure.

 

2D_SLAM

 


nj is the normal vector of tangent line going through point mj. The point-to-line metric minimizes the Euclidean distance from pi to the tangent line through mj, which greatly increase the robustness of the ICP algorithm against sensor noises. Besides, the point-to-line metric can be weighted to reduce the impact of false correspondences caused by outliers and occlusions.

 

 

  • Demo Video

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