MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

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.

Authors

• Binbin Xu 0001
• Wenbin Li 0002
• Dimos Tzoumanikas
• Michael Bloesch
• Andrew J. Davison
• Stefan Leutenegger

Keywords

• Cameras
• Simultaneous localization and mapping
• Tracking
• Motion segmentation
• Semantics
• Dynamics
• Measurement uncertainty
• Dynamic Environment
• Depth Camera
• Object Motion
• Objects In The Scene
• Instance Segmentation
• Camera Pose
• Robust Tracking
• Object Pose
• Camera Pose Estimation
• Point Cloud
• Depth Map
• Static Model
• Tracking Accuracy
• Pose Estimation
• Color Information
• Coordinate Frame
• Static Objects
• Semantic Labels
• Mask R-CNN
• Ray Casting
• Virtual Camera
• World Coordinate
• Current Pose
• Detailed Reconstruction
• Human Pose Estimation
• Object Reconstruction
• Residual Motion
• Finest Level