Advancing Real-World Visual SLAM: Integrating Adaptive Segmentation with Dynamic Object Detection for Enhanced Environmental Perception

The field of Visual Simultaneous Localization and Mapping (VSLAM) has long grappled with the limitations imposed by static environmental assumptions, a challenge that becomes increasingly pronounced in dynamic settings. Addressing this gap, our study introduces a pioneering technique, Accurate and Dynamic Reliable SLAM (YoloV8-SLAM), which represents a paradigm shift in VSLAM technology. This novel approach combines the prowess of dynamic object detection with advanced multi-view geometry, thereby elevating the system’s accuracy, robustness, and efficiency in varied dynamic scenarios. At the core of YoloV8-SLAM is an innovative adaptive segmentation process that skilfully merges geometric motion details from successive frames. This integration not only isolates dynamic objects with heightened precision but also ensures consistent mapping accuracy. The system employs the latest YoloV8 model, synergizing it with an Enhanced Multi-View Geometry algorithm. This combination is adept at handling a spectrum of dynamic conditions, further bolstered by a precise point selection mechanism that swiftly retrieves motion information, crucial for real-time applications. Our extensive evaluations demonstrate that YoloV8-SLAM significantly outperforms existing methods such as including DynaSLAM (Bescos et al., 2018), DM-SLAM (Cheng et al., 2020), RDS-SLAM (Liu et al., 2021), Blitz-SLAM (Fan et al., 2022), and OVD-SLAM (He et al., 2023). Notably, it achieves a 38–45% reduction in Absolute Trajectory Error and a 66–72% decrease in Relative Pose Error, underscoring its superior performance. The breakthrough of YoloV8-SLAM lies in its seamless amalgamation of cutting-edge machine learning with robust geometric principles. This fusion not only facilitates effective dynamic segmentation but also ensures stable structural reconstruction, even in highly dynamic environments. This work marks a substantial advancement in SLAM technology, particularly in addressing the complexities of dynamic and unrestricted settings. YoloV8-SLAM sets a new benchmark in the field and paves the way for future explorations in dynamic SLAM solutions, promising to revolutionize applications requiring high fidelity in real-world, ever-changing environments.