Uncertainty-Guided Proactive Adaptation for Visual-Inertial SLAM

Abstract

Visual--inertial SLAM systems often fail in feature-poor environments such as corridors and textureless walls, leading to catastrophic tracking loss. Existing methods detect degradation reactively after failure occurs, leaving little opportunity for corrective action. We propose a proactive framework that predicts feature degradation 1--2 seconds in advance and adapts sensor fusion weights through uncertainty-guided decisions. Through a systematic comparison of eight temporal architectures across 15,233 sequences, including real robot data, we identify LSTM as the most robust predictor (26.77 MAE). We incorporate uncertainty estimation using Monte Carlo Dropout to enable confidence-aware adaptation thresholds that prevent false adjustments. Our approach provides a foundation for proactive SLAM failure prevention through principled sensor fusion and real-time system adaptation.