论文标题
对象扫描上下文：以对象为中心的空间描述符在3D点云映射中识别位置识别
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
论文作者
论文摘要
SLAM算法与位置识别技术的集成使其能够减轻累积错误并重新定位自身。但是，现有的基于点云的位置识别的方法主要依赖于以激光为中心的描述符的匹配。这些方法具有两个主要缺点：首先，当两个点云之间的距离很重要时，它们无法执行位置识别，其次，它们只能计算旋转角度而无需考虑X和Y方向的偏移。为了克服这些局限性，我们提出了一个围绕主要对象构建的新型局部描述符。通过使用几何方法，我们可以准确计算相对姿势。我们提供了理论分析，以证明该方法可以克服上述局限性。此外，我们对Kitti Odometry和Kitti360进行了广泛的实验，这表明我们提出的方法比最新方法具有显着优势。
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.
