Visual SLAM Based on Object Detection Network: A Review

Abstract

Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering different aspects. Firstly, it discusses the current research status and challenges in this field, highlighting methods for incorporating semantic information from object detection networks into mileage measurement, closed-loop detection, and map construction. It also compares the characteristics and performance of various visual SLAM object detection algorithms. Lastly, it provides an outlook on future research directions and emerging trends in visual SLAM. Research has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal, data association, point cloud segmentation, and other technologies. It can improve the robustness and accuracy of the entire SLAM system and can run in real time. With the continuous optimization of algorithms and the improvement of hardware level, object visual SLAM has great potential for development.