对象扫描上下文：用于三维点云地图内位置识别的对象中心空间描述符
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
AI总结：
针对现有激光雷达中心描述符在距离大时失效且仅算旋转的缺陷，提出围绕主对象的局部描述符，结合几何法精确计算相对位姿，并在KITTI数据集上验证了优越性。
摘要：
将SLAM算法与位置识别技术相结合，使其能够减轻累积误差并实现自身重定位。然而，现有的基于点云的位置识别方法主要依赖于描述符匹配，而这些描述符大多是激光雷达中心的。这些方法存在两个主要缺点：第一，当两个点云之间的距离较大时，它们无法执行位置识别；第二，它们只能计算旋转角度，而不考虑X和Y方向上的偏移。为了克服这些限制，我们提出了一种围绕主对象构建的新型局部描述符。通过使用几何方法，我们可以精确计算相对位姿。我们提供了理论分析，证明该方法能够克服上述限制。此外，我们在KITTI Odometry和KITTI360上进行了大量实验，结果表明我们提出的方法相较于最先进的方法具有显著优势。
英文摘要：
The integration of a SLAM algorithm with place recognition technology empowers it with the ability to mitigate accumulated errors and to relocalize itself. However, existing methods for point cloud-based place recognition predominantly rely on the matching of descriptors, which are mostly lidar-centric. These methods suffer from two major drawbacks: first, they cannot perform place recognition when the distance between two point clouds is significant, and second, they can only calculate the rotation angle without considering the offset in the X and Y directions. To overcome these limitations, we propose a novel local descriptor that is constructed around the Main Object. By using a geometric method, we can accurately calculate the relative pose. We have provided a theoretical analysis to demonstrate that this method can overcome the aforementioned limitations. Furthermore, we conducted extensive experiments on KITTI Odometry and KITTI360, which indicate that our proposed method has significant advantages over state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
