A new spin image based registration algorithm of 3D surrounding model in low-dimensional feature space

Abstract

Most recently, cooperation and coordination among different robots (CCDR), e.g., the air robot and the ground robot, has gradually been a new researching topic in the field of robotics. Among several challenging problems in CCDR, surrounding model registration is very important and difficult, because the models from different robots are usually of different scale and obtained from completely different viewpoints. Currently, very little algorithms have been reported to be feasible for this problem, wherein spin-image based scheme has achieved much attention. However, researches have showed that spin-image based methods present disadvantages in computational efficiency and robustness. Therefore, in this paper, a new spin image based 3D surrounding model registration algorithm is proposed. The new algorithm is on the basis of a three-dimensional feature space, which is composed by the curvature, the Tsallis entropy of spin image, and the reflection intensity of the laser sensor, and combined with the concept of KD-tree to firstly realize the primary key point matching, i.e., to find the corresponding point candidate set (CPCS). After that, spin-image based corresponding point searching is conducted with respect to each CPCS to obtain the accurate corresponding point relation. The most absorbing advantages of the proposed scheme are as the following two aspects: on one hand, due to the three extra features, the fault corresponding relation can be reduced effectively and thus the algorithm precision and robustness can be improved greatly; on the other hand, the CPCS obtained by using the KD-tree method in the constructed low-dimensional feature space contains much less points and thus the computational burden due to spin-image searching is reduced greatly. Finally, in order to verify the feasibility and validity of the proposed algorithm, experiments are conducted and the results are analyzed.