低维特征空间中基于旋转图像的三维环境模型配准方法

近年来,异类机器人之间(如飞行机器人和地面机器人)的协作成为机器人学研究发展的一个新的领域.异类机器人协作的难点之一是协作环境建模,而由于所获得的环境模型具有不同的观测视角和尺度,其环境建模中的模型配准是一个难点和关键.目前,能够适用于大视角差、大尺度差场景配准的方法并不多,基于旋转图像的配准方法被认为是一种可行方案,但其中存在的计算负担大和在野外环境中的鲁棒性差使得其也很难在实际系统中应用.基于此,面向三维点云环境模型,以旋转图像为基础,提出了一种新的基于低维特征空间的模型配准方法.首先,通过引入模型曲率、旋转图像熵值和激光反射强度3个特征构建了一个三维特征空间,得到候选对应点集合.然后,在候选对应点集合中利用旋转图像的方法查找正确的对应关系,实现模型配准.由于低维特征空间的引入,基于旋转图像特征的对应点搜索区域大大减小,因此算法计算效率得到了极大改善.同时由于引入的新特征与场景旋转图像特征的互补性,算法的鲁棒性和精确性也得到了提升.这些性能改进最后通过实验得到了验证.

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

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.