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Mapping and localization are essential capabilities of robotic systems.Although the majority of mapping systems focus on static environments, thedeployment in real-world situations requires them to handle dynamic objects. Inthis paper, we propose an approach for an RGB-D sensor that is able toconsistently map scenes containing multiple dynamic elements. For localizationand mapping, we employ an efficient direct tracking on the truncated signeddistance function (TSDF) and leverage color information encoded in the TSDF toestimate the pose of the sensor. The TSDF is efficiently represented usingvoxel hashing, with most computations parallelized on a GPU. For detectingdynamics, we exploit the residuals obtained after an initial registration,together with the explicit modeling of free space in the model. We evaluate ourapproach on existing datasets, and provide a new dataset showing highly dynamicscenes. These experiments show that our approach often surpass otherstate-of-the-art dense SLAM methods. We make available our dataset with theground truth for both the trajectory of the RGB-D sensor obtained by a motioncapture system and the model of the static environment using a high-precisionterrestrial laser scanner. Finally, we release our approach as open sourcecode.

ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals