Reducing Redundancy in VSLAM: VLMs-driven Keyframe Selection using Multi-dimensional Semantic Information

Abstract

Keyframe selection plays a crucial role in balancing computational efficiency and localization accuracy in Visual Simultaneous Localization and Mapping (VSLAM) systems. Existing keyframe selection methods often struggle to capture high-level semantic information in environments where multiple semantic dimensions interact. In this paper, we propose the Multi-dimensional Semantic Analysis (MSA) module based on Visual-Language Models (VLMs). By leveraging the capability of VLMs to extract rich semantic features, we compute the similarity between each image frame and a set of textual descriptions, generating a scene descriptor that quantifies the semantic distance between frames across multiple dimensions (e.g., object count, texture, and lighting). We then introduce the Scene Change Assessment (SCA) module based on Bayesian On-line Changepoint Detection (BOCD), which identifies keyframes with significant semantic information gain, thereby reducing the total number of keyframes. Extensive experiments on an open dataset demonstrate that our method not only significantly reduces the number of keyframes but also maintains high localization accuracy. Furthermore, the inference speed of the MSA module satisfies the real-time requirements of VSLAM. These results underscore the potential of our approach to enhance the efficiency of keyframe selection.