# Object-Level Semantic Map Construction for Dynamic Scenes
## 2 Related Work
### Object Semantic SLAM  
The computer graphics and vision communities have devoted substantial efforts to object and scene segmentation. Segmented data can broaden the functionality of visual tracking and mapping systems, for instance, by enabling robots to detect objects. The task of target detection is applied to find all the targets or objects of interest in the image and to determine their positions and sizes, which is one of the core issues in machine vision. The traditional target detection process often utilizes a sliding window for region selection and then applies SIFT, HOG, or another method for feature extraction. Finally,the support vector machine approach and Adaboost are adopted for category judgment. However, the traditional method has poor robustness, making it incapable of reflecting the illumination changes, background diversity, etc. In addition, the regional selection is not targeted, the time complexity is high, and the window is redundant. Some researchers have proposed segmenting RGBD data based on the geometric properties of surface normals, mainly by assuming that objects are convex. [PERSON] et al. [15] successfully segmented 3D laser data based on this assumption. The same principle has also been used by other authors, in order to segment objects in RGBD frames [16; 17; 18; 19; 20]. The significant recent progress in deep learning can provide solutions involving the utilization of semantic spatial information. From the RCNN to the Fast RCNN and Faster RCNN, deep learning methods represented by RCNNs in target detection have sharply improved the target detection accuracy. Subsequently, [PERSON] et al. added a fully convolutional network to generate the corresponding MASK branch based on the original Faster RCNN algorithm. The Mask RCNN method can be used to accomplish various tasks such as target classification, target detection, and semantic segmentation. Deep learning neural networks are used to detect and segment objects in visual SLAM and build object-level semantic maps of scenes, achieving excellent results. [PERSON] et al. proposed a method of constructing a dense 3D semantic map called semantic fusion based on a convolutional neural network, which relied on the elastic fusion SLAM algorithm to provide indoor RGB-D video interframe for position-pose estimation, utilizing a convolutional neural network to predict the pixel-level object category label and ultimately combining the Bayesian upgrade strategy with the conditional random field model. This approach enabled the probability escalation of CNN prediction values from different perspectives and finally generated a dense 3D semantic map containing semantic information. [PERSON] et al. [21] proposed a monocular and semi-dense 3 Dsemantic mapping construction method based on a CNN and large scale direct (LSD) SLAM, where the LSD-SLAM method was used instead of the elastic fusion method to estimate the camera pose. [PERSON] et al. [22] proposed an object-oriented mapping system composed of instances using bounding box detection from a CNN and an unsupervised geometric segmentation algorithm using RGB-D data. SLAM++ by [PERSON] et al. [23] was an early RGB-D object-oriented mapping system. They used point pair features for object detection and a pose graph for global optimization. The drawback was the requirement that the full set of object instances, with their very detailed geometric shapes, had to be known beforehand and preprocessed in an offline stage before running the algorithm. [PERSON] and [PERSON] [24] also previously tracked object models learned beforehand by registering them to a multi-resolution surfel map. [PERSON] et al. [25] used a pre-trained database of objects to generate descriptors, but they used a KinectFusion [1] TSDF to segment regions of a reconstructed TSDF volume incrementally and to match 3D descriptors directly against those of other objects in the database. In RGB-only SLAM for object detection, [PERSON] and [PERSON] [26] used ORB-SLAM [27] to assist object recognition.