RDynaSLAM: Fusing 4D Radar Point Clouds to Visual SLAM in Dynamic Environments

The performance of visual SLAM systems, in terms of both robustness and accuracy, can be affected by the presence of dynamic objects in dynamic environments. The utilization of learning-based dynamic SLAM algorithms introduces additional challenges, such as increased power consumption and computing requirements, particularly on mobile platforms. Millimeter wave radar has the capability to directly detect and measure the relative velocity between objects and the radar system. Therefore, this paper presents a novel SLAM system that aims to integrate millimeter wave radar point clouds into visual SLAM in a dynamic environment. First, a real-time dynamic cluster extraction method was developed using Doppler information obtained from 4D radar. It effectively distinguishes between static background points and dynamic points by employing the RANSAC algorithm. The dynamic radar points are subsequently grouped together to create dynamic clusters. Then, the clusters are projected onto the image and expand to produce dynamic masks, taking into account the distribution characteristics. Finally, dynamic masks are employed to eliminate dynamic keypoints during the camera pose estimation, allowing for the estimation to be based solely on static keypoints. Experiments conducted in various daily dynamic scenarios have demonstrated the robustness of RDynaSLAM in operating effectively within dynamic environments. In comparison to ORBSLAM3, RDynaSLAM exhibits a notable reduction in the Root Mean Square Error (RMSE) of Absolute Pose Error (APE) and Relative Pose Error (RPE) in high dynamic environments. The method proposed in this paper has the capability to operate in real-time, without the need for GPU utilization.