Game-KFS: Game-Theory-Inspired Keyframe Selection for Hybrid Representation Visual SLAM

Abstract

Hybrid representation Visual Simultaneous Localization and Mapping (VSLAM) systems combine the inherent strengths of both discrete and field representations. They promise high-precision tracking and photo-realistic dense mapping. However, current keyframe selection methods in hybrid representation VSLAM struggle to satisfy both the high-precision tracking requirements of discrete representations and the high-quality rendering requirements of field representations. In this paper, we propose a game-theory-inspired keyframe selection approach that addresses the requirements of both representation types. We introduce two objective functions to comprehensively assess discrete point tracking and radiance field model rendering. By employing a game-theory-inspired framework, our method effectively balances these objectives to achieve improved keyframe selection. Experimental results demonstrate that integrating our approach into a hybrid representation VSLAM system significantly enhances tracking accuracy and rendering quality, outperforming existing keyframe selection methods.