Imperial College London


Faculty of EngineeringDepartment of Computing

Senior Lecturer



+44 (0)20 7594 7123s.leutenegger Website




360ACE ExtensionSouth Kensington Campus






BibTex format

author = {Xu, B and Li, W and Tzoumanikas, D and Bloesch, M and Davison, A and Leutenegger, S},
publisher = {IEEE},
title = {MID-fusion: octree-based object-level multi-instance dynamic SLAM},
url = {},
year = {2019}

RIS format (EndNote, RefMan)

AB - We propose a new multi-instance dynamic RGB-D SLAM system using anobject-level octree-based volumetric representation. It can provide robustcamera tracking in dynamic environments and at the same time, continuouslyestimate geometric, semantic, and motion properties for arbitrary objects inthe scene. For each incoming frame, we perform instance segmentation to detectobjects and refine mask boundaries using geometric and motion information.Meanwhile, we estimate the pose of each existing moving object using anobject-oriented tracking method and robustly track the camera pose against thestatic scene. Based on the estimated camera pose and object poses, we associatesegmented masks with existing models and incrementally fuse correspondingcolour, depth, semantic, and foreground object probabilities into each objectmodel. In contrast to existing approaches, our system is the first system togenerate 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 aCPU, excluding the instance segmentation part. We demonstrate its effectivenessby quantitatively and qualitatively testing it on both synthetic and real-worldsequences.
AU - Xu,B
AU - Li,W
AU - Tzoumanikas,D
AU - Bloesch,M
AU - Davison,A
AU - Leutenegger,S
PY - 2019///
TI - MID-fusion: octree-based object-level multi-instance dynamic SLAM
UR -
UR -
ER -