A dynamic detection and data association method based on probabilistic models for visual SLAM

Visual Simultaneous Localization and Mapping (VSLAM) is a critical foundation in mobile robotics and augmented reality (AR). However, VSLAM faces challenges in dynamic environments since both the camera and the object are in motion, which contradicts the classical static scene assumption. Generally, multi-view geometry is employed for static features to estimate camera pose and reconstruct environment maps. Hence, dynamic feature detection and data association become key issues in dynamic VSLAM. To solve these problems, we propose an innovative probability-based approach that combines instance segmentation and nonparametric Kolmogorov–Smirnov test approaches to detect the distribution of features on an object. Furthermore, we propose a data association algorithm based on the Bayesian model that comprehensively utilizes the descriptors of feature points and their spatial information. Experiments on the KITTI public dataset and the Oxford Multi-motion dataset validate the effectiveness of our method.