RSO-SLAM: A Robust Semantic Visual SLAM With Optical Flow in Complex Dynamic Environments

Visual Simultaneous Localization and Mapping (VSLAM) has undergone gradual development and found widespread application. However, existing VSLAM systems predominantly rely on static environment assumptions, leading to diminished robustness and localization accuracy in the presence of dynamic elements. Previous research has primarily employed geometric and semantic constraints to address dynamic regions of the scene. Nevertheless, their efficacy is limited in complex dynamic scenarios involving non-rigid objects, non-predefined motion targets, and low dynamic motion targets. Furthermore, the majority of dynamic SLAM methods are predominantly designed for indoor RGBD environments, resulting in a lack of generalizability. In this paper, a dynamic SLAM method that combines instance segmentation and optical flow called RSO-SLAM is proposed. RSO-SLAM is designed to operate effectively in diverse complex motion scenarios, both indoors and outdoors, and supports various visual sensor modes, including monocular, stereo, and RGBD setups. The proposed approach amalgamates semantic information and optical flow data by employing a "KMC:k-means $+$ connectivity" based algorithm for motion region detection within the scene. Furthermore, it integrates an optical flow attenuation propagation strategy to facilitate meticulous motion probability computations and inter-frame propagation within each identified region. Our methodology's superiority over existing dynamic SLAM approaches is firmly established through comprehensive evaluations across a diverse range of intricate dynamic scenarios. These evaluations encompass various conditions of high and low dynamism in both indoor and outdoor environments, accompanied by rigorous ablation experiments and real-world assessments. RSO-SLAM exhibits enhanced robustness and higher localization accuracy, rendering it well-suited for nearly all dynamic environments.