Real-time 3D reconstruction from RGB-D sensor data plays an important role in many robotic applications, such as object modeling and mapping. The popular method of fusing depth information into a truncated signed distance function (TSDF) and applying the marching cubes algorithm for mesh extraction has severe issues with thin structures: not only does it lead to loss of accuracy, but it can generate completely wrong surfaces. To address this, we propose the directional TSDF - a novel representation that stores opposite surfaces separate from each other. The marching cubes algorithm is modified accordingly to retrieve a coherent mesh representation. We further increase the accuracy by using surface gradient-based ray casting for fusing new measurements. We show that our method outperforms state-of-the-art TSDF reconstruction algorithms in mesh accuracy.
We introduce a novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner. In particular, we propose a fast scan registration algorithm that operates on 2D maps represented as a signed distance function (SDF). Using SDFs as a map representation has several advantages over existing approaches: while classical 2D scan matchers employ brute-force matching to track the position of the robot, signed distance functions are differentiable on large parts of the map. Consequently, efficient minimization techniques such as Gauss-Newton can be applied to find the minimum. In contrast to occupancy grid maps, the environment can be captured with sub-grid cell size precision, which leads to a higher localization accuracy. Furthermore, SDF maps can be triangulated to polygon maps for efficient storage and transfer. In a series of experiments, conducted both in simulation and on a real physical platform, we demonstrate that SDF tracking is more accurate and efficient than previous approaches. We outperform scan matching on occupancy maps in simulation by ∼270 in terms of root mean squared deviation (RMSD) with a ∼63 lower standard deviation. In the real robot experiments, we obtain a performance advantage of ∼14 RMSD with a ∼25 lower standard deviation. Mesh plays an indispensable role in dense realtime reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an efficient incremental framework specifically designed for online mesh storage and manipulation is missing. We propose a novel framework to compactly generate, update, and refine mesh for scene reconstruction upon a volumetric representation. Maintaining a spatial-hashed field of cubes, we distribute vertices with continuous value on discrete edges that support O(1) vertex accessing and forbid memory redundancy. By introducing Hamming distance in mesh refinement, we further improve the mesh quality regarding the triangle type consistency with a low cost. Lock-based and lock-free operations were applied to avoid thread conflicts in GPU parallel computation. Experiments demonstrate that the mesh memory consumption is significantly reduced while the running speed is kept in the online reconstruction process. 