Spatial change detection using normal distributions transform

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.