A User-Guided Framework for Visual Odometry Algorithm Selection and Composition

Visual odometry (VO) plays a critical role in unmanned systems, as its performance directly determines localization accuracy and stability. However, existing visual odometry selection methods often rely on expert knowledge or single-performance metrics, limiting adaptability under diverse user preferences, diverse environments, and device constraints. This paper proposes a user-guided framework for visual odometry algorithm selection and composition, named UG-VOS, which models the selection process as a cognitive decision-making system driven by user preferences and environmental context. First, We design a scene-adaptive selection module that embeds lightweight adapters into a Transformer encoder to adaptively model environmental features and jointly optimize front-end and back-end algorithm selection. Second, we propose a multiobjective evaluation module that integrates user preferences with environmental context and visual odometry performance to guide algorithm selection, achieving a balanced trade-off among accuracy, efficiency, and system resource usage. Experiments on diverse datasets demonstrate that UG-VOS consistently achieves superior algorithm compositions compared to existing methods, offering a robust trade-off between precision and resource efficiency.