OASIS: Optimized Adaptive System for Intelligent SLAM

Visual Simultaneous Localization and Mapping (VSLAM) is essential for mobile autonomous systems operating in complex dynamic environments. VSLAM algorithms are computationally intensive and must execute in real-time on resource-constrained embedded devices. Variations in environmental complexity can lead to longer frame processing times, causing dropped frames, lost localization information, and degraded accuracy. To address these challenges, we introduce OASIS, a novel adaptive approximation method that dynamically reduces input frame areas based on realtime visual importance. Unlike traditional optimizations that require adjusting internal SLAM parameters, OASIS selectively minimizes computation by adaptively filtering less critical image regions, significantly reducing computational load. Evaluations on the EuRoC MAV dataset demonstrate that our approach balances accuracy and system predictability, achieving up to a 71.8% reduction in worst-case pose estimation errors. OASIS offers a significant advancement in reliable, predictable, and energy-efficient SLAM tailored for mobile autonomous robotic applications.