# Object-Level Semantic Map Construction for Dynamic Scenes
###### Abstract  
Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes. object tracking instance segmentation; dynamic simultaneous localization and mapping; camera pose tracking 2021 11 645 11.12.12. Dense camera pose tracking does not rely on the geometric features of the environment, but rather fully utilizes the geometric structure and texture information of the environment. Compared with the traditional SLAM method based on geometric features, dense camera pose tracking is much more robust in complex environments. State-of-the-art dense visual SLAM methods can produce impressive reconstructions of large indoor scenes. However, few mainstream algorithms for dense visual SLAM utilize object-level semantic information. Thinking of a scene as a representation of objects, we argue that this approach is a natural and efficient way to represent the things that are the most important for robotic scene understanding, mission planning, and physical interaction. It is also highly suitable as the basis for human-robot communication. Therefore, object-level SLAM has attracted considerable attention in recent years, in addition to being the development trend of visual SLAM. However, most such methods have difficulty obtaining better performance in dynamic environments, because moving objects can significantly interfere with positioning, scene segmentation, and map construction. If not actively segmented and detected, dynamic objects can be fused into the map, which can lead to irreversible corruption. To design robots that can interact with dynamic scenes smoothly, it is crucial to equip them with the abilities to (i) discover static and dynamic objects in a scene via segmentation and motion detection and (ii) track and estimate the 3D geometry of each object independently in a dynamic environment. These high-level, object-based semantic scene representations would greatly enhance the perception and physical interaction capabilities of robots. In this work, we developed an object-oriented dense online SLAM system constructed based on ElasticFusion [5] with a focus on indoor scene understanding using RGB-D data. The semantic RGB-D visual SLAM architecture is integrated with a deep learning network with enhanced semantic instance segmentation and object motion detection for location and mapping in complex dynamic environments. Our objectives were to take a live stream of RGB-D frame data as input, to obtain the accurate camera pose, and to segment the scene into different objects while simultaneously tracking and reconstructing the 3D shapes of the objects in a dynamic environment. According to prior knowledge of previously constructed object model databases, the camera pose can be estimated roughly.