Spatial change detection is a fundamental technique for finding the differences between two or more pieces of geometrical information. This technique is critical in some robotic applications, such as search and rescue, security, and surveillance. In these applications, it is desirable to find the differences quickly and robustly. The present paper proposes a fast and robust spatial change detection technique for a mobile robot using an on-board range sensors and a highly precise 3D map created by a 3D laser scanner. This technique first converts point clouds in a map and measured data to grid data (ND voxels) using normal distributions transform. The voxels in the map and the measured data are then compared according to the features of the ND voxels. Three techniques are introduced to make the proposed system robust for noise, that is, classification of point distribution, overlapping of voxels, and voting using consecutive sensing. The present paper shows the results of indoor and outdoor experiments using an RGB-D camera and an omni-directional laser scanner mounted on a mobile robot to confirm the performance of the proposed technique.

中文翻译：

---

使用正态分布变换的空间变化检测

空间变化检测是用于发现两个或更多几何信息之间的差异的基本技术。这项技术在某些机器人应用中至关重要，例如搜索和救援，安全性和监视。在这些应用中，期望快速而稳健地找到差异。本文提出了一种用于移动机器人的快速而稳健的空间变化检测技术，该技术使用板载距离传感器和由3D激光扫描仪创建的高精度3D地图。该技术首先使用正态分布变换将地图中的点云和测量数据转换为网格数据（ND体素）。然后根据ND体素的特征比较地图中的体素和测量数据。引入了三种技术来使建议的系统具有较强的抗噪能力，即，使用连续感测对点分布进行分类，体素重叠以及投票。本文显示了使用RGB-D相机和安装在移动机器人上的全方位激光扫描仪进行室内和室外实验的结果，以确认所提出技术的性能。