Place recognition technology endows a SLAM algorithm with the ability to eliminate accumulated errors and to relocalize itself. Existing methods on point cloud-based place recognition often leverage the matching of global descriptors which are lidar-centric. These methods have the following two major defects: place recognition cannot be performed when the distance between the two point clouds is far, and only the rotation angle can be calculated without the offset in the X and Y direction. To solve these two problems, we propose a novel global descriptor, which is built around the Main Object, in this way, descriptors are no longer dependent on the observation position. We analyze the theory that this method can perfectly solve the above two problems, and conduct a lot of experiments in KITTI and some extreme scenarios, which show that our method has obvious advantages over traditional methods.
翻译:定位识别技术使SLAM算法具有消除累积错误和重新定位的能力。 点云定位的现有方法往往会影响全球描述器的匹配, 这些方法有以下两个主要缺陷: 当两个点云的距离很远时, 无法进行定位, 只有旋转角度可以计算而不在 X 和 Y 方向中抵消。 为了解决这两个问题, 我们提议了一个新的全球描述器, 其构建方式是, 描述器不再依赖于观察位置。 我们分析这一方法能够完全解决以上两个问题的理论, 在 KITTI 和一些极端假设中进行大量实验, 这表明我们的方法对传统方法具有明显的优势 。