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

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 letter, 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.