Multi-resolution surfel maps for efficient dense 3D modeling and tracking
Building consistent models of objects and scenes from moving sensors is an important prerequisite for many recognition, manipulation, and navigation tasks. Our approach integrates color and depth measurements seamlessly in a multi-resolution map representation. We process image sequences from RGB-D cameras and consider their typical noise properties. In order to align the images, we register view-based maps efficiently on a CPU using multi-resolution strategies. For simultaneous localization and mapping (SLAM), we determine the motion of the camera by registering maps of key views and optimize the trajectory in a probabilistic framework. We create object models and map indoor scenes using our SLAM approach which includes randomized loop closing to avoid drift. Camera motion relative to the acquired models is then tracked in real-time based on our registration method. We benchmark our method on publicly available RGB-D datasets, demonstrate accuracy, efficiency, and robustness of our method, and compare it with state-of-the-art approaches. We also report on several successful public demonstrations where it was used in mobile manipulation tasks.