Removing Moving Objects from Point Cloud Scenes

Three-dimensional simultaneous localization and mapping is a topic of significant interest in the research community, particularly so since the introduction of cheap consumer RGB-D sensors such as the Microsoft Kinect. Current algorithms are able to create rich, visually appealing maps of indoor environments using such sensors. However, state-of-the-art systems are designed for use in static environments. This restriction means, for instance, that there can be no people moving around the environment while the mapping is being done. This severely limits the application space for such systems. To address this issue, we present an algorithm to explicitly identify and remove moving objects from multiple views of a scene. We do this by finding corresponding objects in two views of a scene. If the position of an object with respect to the other objects changes between the two views, we conclude that the object is moving and should therefore be removed. After the algorithm is run, the two views can be merged using any existing registration algorithm. We present results on scenes collected around a university building.