Reshaping our model of the world over time

An accurate estimate of the 3D-structure in the environment is key to robotic applications such as autonomous inspection, obstacle avoidance and manipulation. Recent years have seen substantial algorithmic advances towards creating highly accurate models of small objects as well as large scale architectural structures. Most commonly a rich set of images covering a static scene are used to jointly estimate the pose of the cameras and the observed 3D-structure. For many practical application however the assumption of static scenes and sufficient coverage by images does not hold. In fact for industrial inspection the change in the scene is of most interest and the limited resources on mobile platforms don't allow for extensive data captures. In this paper we investigate the potential of combining multiple independent captures of a place to selectively reconstruct a scene over time. We propose an incremental reconstruction algorithm which identifies and fuses novel data into a joint model of the scene. Being able to identify changing parts of the scene is particularly interesting for mobile applications where bandwidth, storage and processing power are limited. Through detailed experiments, we show the potential of our approach to use multiple mobile devices to reconstruct and update a model of the static part of the environment over time.