# Object-Level Semantic Map Construction for Dynamic Scenes
## 2 Related Work
### Dynamic SLAM  
Despite this progress, much work is still based on the fundamental assumption of a static environment, within which points in the 3D world always maintain the same spatial positions in the global world, with the only moving object being the camera. Most odometry methods perform registration between the current image and a previous reference, and the estimated transformation between these images is assumed to be due to the camera motion. Traditional SLAM algorithms are based on this assumption and achieving performance results, whereas robots tend to be artificial dynamic environments and are more concerned with dynamic objects, such as people, animals, and vehicles. Therefore, the recognition and 3D tracking of dynamic objects are the core issues of modern SLAM systems. Since it became possible to obtain better visual SLAM results in static environments, some studies have addressed visual SLAM in dynamic environments. There are three ways to deal with dynamic objects in visual SLAM. One deforms the whole world in a non-rigid manner in order to include a deformable/moving object [4]. The second specifically aims at building a single static background model while ignoring all possibly moving objects and, thus, improving the camera tracking accuracy [30; 31; 32; 33]. For example, DynaSLAM [33] uses a CNN to identify dynamic objects and remove them from the image to avoid dynamic object interference. [PERSON] et al. [34] proposed a method of detecting potentially dynamic objects using semantic information and then using a contour refinement algorithm to detect objects more accurately. [PERSON] et al. [35; 36] proposed a motion removal approach to improve RGB-D SLAM in dynamic environments, as well as a background modeling method to segment foreground from background. This approach was mostly demonstrated on driving scenarios and was limited to sparse reconstructions. StaticFusion [31] performs segmentation by coupling camera motion residuals, depth inconsistency, and a regularization term. In the implicit handling of dynamic elements, it is common to use a robust cost function within the visual odometry front end, which penalizes the contributions of high-residual points and implicitly increases the robustness of pose estimation against un-modelled effects. Meanwhile, [PERSON] et al. [10; 13] demonstrated the robustness against the presence of small moving objects in the scene. However, these solutions are insufficient and fail when moving parts occupy a significant portion of the image. The third means of dealing with dynamic objects in visual SLAM models the dynamic components by creating sub-maps for every possibly rigidly moving object in the scene while fusing corresponding information into these sub-maps [37; 38]. The SLAMMOT project [39] represented an important step towards extending the SLAM framework to dynamic environments by incorporating the detection and tracking of moving objects into the SLAM operation. Only very recently has the problem of reconstruction of dense dynamic scenes in real time been addressed. [PERSON] et al. proposed the MID-Fusion [40] system, which is a new multi-instance dynamic RGBD SLAM system using a Mask R-CNN to find semantic instances and an object-level octree-based volumetric representation. Co-fusion [37] is another real-time dense mapping system capable of reconstructing both a static background map and dynamic objects from a scene. The corresponding paper presented a real-time pipeline that can segment and track dynamic objects based on either motion or semantic cues, reconstructing them separately using a surfel-based representation. DynSLAM [33] utilizes a mapping system for autonomous driving applications capable of separately reconstructing both the static environment and moving vehicles. However, the overall system is not real-time and vehicles constitute the only dynamic object class reconstructed, so the functionality of this approach is limited to road scenes. [PERSON] et al.