Abstract Aiming at the complicated unstructured indoor environment and the dynamic service task of robots, an environment cognition method based on quick response (QR) code technology is proposed. Under the premise of gaining depth information with binocular vision, the uncertain mathematic model of binocular vision information is established on Dezert-Smarandache theory (DSmT) evidence theory. And the three-dimensional grid map is formed which describes the occupied/free probability of the voxel. While the three-dimensional map is structured, the semantic labels for the large objects are added by QR code based object mark plastered on large objects. The occupied/free values of the corresponding voxels are updated based on the dimension of the large object. Then, the three-dimensional grid semantic map is formed which includes the function property and the attributive relation of the large objects. The experiments are carried out to compare several information fusion algorithms with the proposed method and analyze the recognition accuracy for the artificial object marks. The results prove the availability and feasibility of the proposed method.
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2012, Vol. 25 
