  We present DetectFusion, an RGB-D SLAM system that runs in real-time and can
robustly handle semantically known and unknown objects that can move
dynamically in the scene. Our system detects, segments and assigns semantic
class labels to known objects in the scene, while tracking and reconstructing
them even when they move independently in front of the monocular camera. In
contrast to related work, we achieve real-time computational performance on
semantic instance segmentation with a novel method combining 2D object
detection and 3D geometric segmentation. In addition, we propose a method for
detecting and segmenting the motion of semantically unknown objects, thus
further improving the accuracy of camera tracking and map reconstruction. We
show that our method performs on par or better than previous work in terms of
localization and object reconstruction accuracy, while achieving about 20 FPS
even if the objects are segmented in each frame.
