We propose a new multi-instance dynamic RGB-D SLAM system using an
object-level octree-based volumetric representation. It can provide robust
camera tracking in dynamic environments and at the same time, continuously
estimate geometric, semantic, and motion properties for arbitrary objects in
the scene. For each incoming frame, we perform instance segmentation to detect
objects and refine mask boundaries using geometric and motion information.
Meanwhile, we estimate the pose of each existing moving object using an
object-oriented tracking method and robustly track the camera pose against the
static scene. Based on the estimated camera pose and object poses, we associate
segmented masks with existing models and incrementally fuse corresponding
colour, depth, semantic, and foreground object probabilities into each object
model. In contrast to existing approaches, our system is the first system to
generate an object-level dynamic volumetric map from a single RGB-D camera,
which can be used directly for robotic tasks. Our method can run at 2-3 Hz on a
CPU, excluding the instance segmentation part. We demonstrate its effectiveness
by quantitatively and qualitatively testing it on both synthetic and real-world
sequences.