Abstract
As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.
Highlights
The positioning method based on LiDAR is one of the leading technologies applied to automated guided vehicle (AGV) positioning
The positioning method based on LiDAR is one of the leading technologies applied to AGV positioning
The electromagnetic AGV positioning technology is more mature, while the positioning system based on LiDAR and vision is gradually applied to AGV with research developments [2]
Summary
The positioning method based on LiDAR is one of the leading technologies applied to AGV positioning. The LiDAR positioning systems are usually based on artificial landmarks and natural features. LiDAR positioning systems based on artificial landmarks rely on reflectors with high reflection intensity and the three-point positioning method [10]. A reflector matching method was proposed according to the distance between reflectors, which can complete the reflector matching in 0.3 s This algorithm was not applied to practical positioning, and the performance remains unknown. The outer layer of EKF uses the result to correct and map with LiDAR data by further estimating the position and pose of AGV. According to the present problems of AGV LiDAR positioning methods, this paper improves the traditional ICP algorithm and proposes an AGV positioning method based on the ICP algorithm and assisted by artificial landmarks.
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