## 2 System Description  
The system framework of this paper as shown in Figure 1 can be divided into five main components, i.e., tracking thread, localized mapping, loop closing, dynamic object detection thread, and dense map building. The tracking thread is in charge of tracking the localization and orientation of the camera in real-time. This component matches features using ORB (Oriented FAST and Rotated BRIEF) feature points to estimate the camera pose. Pose estimation, descriptor matching, and feature extraction are steps in the feature matching process. For the purpose of creating a local map, keyframe insertion and deletion are mainly handled by the local mapping thread. It also optimizes the data generated by the tracking thread to increase localization accuracy [25]. The job of the loop closing thread is to check if the camera has returned to a previously visited area on a regular basis, which would indicate a loop closure event. The loop closure detection thread initiates a loop closure operation in the event that a loop closure is found. The dynamic object detection thread uses the YOLOv5s network as its main body, replacing YOLOv5's Backbone with MobileNetV3-Small to lighten the overall network structure [26]. To create the dense point cloud map, the thread responsible for building the dense map must first convert the depth and image information into 3D point cloud data. Next, it must color the point cloud using the RGB image's color information and stitch the point cloud together using keyframes. The tracking thread extracts ORB feature points from the input image. ORB is an algorithm that identifies and describes important feature points in an image. The two basic components of ORB feature extraction are BRIEF (Binary Robust Independent Elementary Features) feature description and FAST (Features from Accelerated Segment Test) feature point detection. FAST is a high-speed algorithm for detecting corner points in an image. The FAST algorithm first selects one pixel in the image to act as the center pixel before determining corner points. To determine whether this center pixel is a corner point, its intensity values are compared to those of the surrounding pixels. As shown in Figure 2, the center pixel point \(p\) has a gray scale of \(I_{P}\), and the FAST algorithm selects a fixed number of surrounding pixels, usually 16 pixels. It then compares the gray values of these surrounding pixels with the gray value of the center pixel. If there are 12 consecutive points whose gray values satisfy Equation (1), then the point is a corner point. \[\sum\
olimits_{x\in circle(p)}|I_{X}-I_{P}|>T \tag{1}\]  
where \(T\) is the threshold value and \(I_{x}\) represents the gray level of the surrounding 16 pixel points. Figure 1: Framework overview of our proposed SLAM. The algorithm known as BRIEF is chosen by ORB to describe the feature. BRIEF's primary objective is to produce robust and efficient binary feature descriptors that may be used to match and identify important or distinctive points in a picture. BRIEF selects a feature point and determines a \(T\times T\) window and then randomly selects n pairs of pixel points defined as  
\[\tau(p:x,y)= \begin{cases}1&p(x)<p(y)\\ 0&p(x)\geq p(y)\end{cases}  \tag{2}\]  
where \(p(x)\) and \(p(y)\) are the gray values at point \(x\) and \(y\), respectively. Then, the descriptor of feature point \(p\) is defined as  
\[f_{n}(p)=\sum_{1\leq i\leq n}2^{i-1}\tau(p:x_{i},y_{i}) \tag{3}\]  
One of the key components of the system is feature matching; the accuracy of the location estimation can only be ensured when the feature points are matched accurately and efficiently. The camera position problem must be solved when feature matching is finished, and the method in this work uses the ICP (Iterative Closest Point) algorithm to estimate the position of 3D feature points.