  Mapping and localization are essential capabilities of robotic systems.
Although the majority of mapping systems focus on static environments, the
deployment in real-world situations requires them to handle dynamic objects. In
this paper, we propose an approach for an RGB-D sensor that is able to
consistently map scenes containing multiple dynamic elements. For localization
and mapping, we employ an efficient direct tracking on the truncated signed
distance function (TSDF) and leverage color information encoded in the TSDF to
estimate the pose of the sensor. The TSDF is efficiently represented using
voxel hashing, with most computations parallelized on a GPU. For detecting
dynamics, we exploit the residuals obtained after an initial registration,
together with the explicit modeling of free space in the model. We evaluate our
approach on existing datasets, and provide a new dataset showing highly dynamic
scenes. These experiments show that our approach often surpass other
state-of-the-art dense SLAM methods. We make available our dataset with the
ground truth for both the trajectory of the RGB-D sensor obtained by a motion
capture system and the model of the static environment using a high-precision
terrestrial laser scanner. Finally, we release our approach as open source
code.
