  The majority of approaches for acquiring dense 3D environment maps with RGB-D
cameras assumes static environments or rejects moving objects as outliers. The
representation and tracking of moving objects, however, has significant
potential for applications in robotics or augmented reality. In this paper, we
propose a novel approach to dynamic SLAM with dense object-level
representations. We represent rigid objects in local volumetric signed distance
function (SDF) maps, and formulate multi-object tracking as direct alignment of
RGB-D images with the SDF representations. Our main novelty is a probabilistic
formulation which naturally leads to strategies for data association and
occlusion handling. We analyze our approach in experiments and demonstrate that
our approach compares favorably with the state-of-the-art methods in terms of
robustness and accuracy.
