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A User-Guided Framework for Visual Odometry Algorithm Selection and Composition
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AI
DOI：10.1109/tcds.2026.3667590.png)
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Abstract
En 中文
Visual odometry (VO) plays a critical role in unmanned systems, as its performance directly determines localization accuracy and stability. However, existing VO selection methods often rely on expert knowledge or single-performance metrics, limiting adaptability under diverse user preferences, diverse environments, and device constraints. This article proposes a user-guided framework for VO 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 VO 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.
Keywords:
Algorithm composition
algorithm selection
cognitive systems
user-guided decision making
visual odometry (VO)
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