Dynamic SLAM Visual Odometry Based on Instance Segmentation: A Comprehensive Review

Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation, dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object, thereby reducing the impact of dynamic objects on the SLAM system’s positioning. This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation, comparing and summarizing their advantages and disadvantages. Through comparisons on datasets, it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments. However, the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems. In recent years, the rapid development of single-stage instance segmentation methods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation. Finally, possible future research directions and improvement measures are discussed for reference by relevant professionals.