Optimized Feature Points and Keyframe Methods for VSLAM in High-Dynamic Indoor Environments

Abstract

VSLAM is one of the key technologies for indoor mobile robots, used to perceive the surrounding environment, achieve accurate positioning and mapping. However, traditional VSLAM algorithms based on the assumption of a static environment still face certain challenges. The movement, occlusion, and appearance changes of dynamic objects can lead to feature point-matching errors, making data association difficult and causing biases in motion estimation. In order to address this challenge, this paper proposes a dynamic feature point removal method and a closed-loop detection method for high dynamic scenes, aiming to effectively improve the robustness and positioning accuracy in dynamic environments. First, the YOLOv7-tiny object detection network and LK optical flow algorithm are combined to detect the dynamic area, and the adaptive threshold keyframe selection method is adopted to solve the problem of poor quality of keyframe caused by the existing heuristic threshold selection method. Then, this paper proposes a dynamic keyframe sequence creation method based on the angle difference between keyframes, which reduces the workload of loop back detection and accelerates the efficiency of loop back detection in the system. Next, the ParC_NetVLAD image matching algorithm is proposed. In this paper, ConvNeXt-Tiny network is used for feature extraction of images, and ParC-Net network and CBAM attention mechanism are added to the feature extraction network. Finally, NetVLAD is used to cluster the extracted local features to obtain global features that can represent images. Experiments are conducted on public TUM RGB-D datasets and in real-world situations. The proposed algorithm reduces the ATE (Absolute Trajectory Error) by 96.4% and the RPE (Relative Trajectory Error) by 82.8% on average in highly dynamic scenarios. In the Pittsburgh30k dataset, the average accuracy of loop closure detection has been improved by 2.6%.