Bio-Inspired Gait-Adaptive Mapping: Real-Time 3-D Scene Modeling Through Embodied Sensorimotor Coordination

To address the challenges posed by periodic gait-induced disturbances in legged robotic systems, we propose an enhanced visual SLAM framework that systematically incorporates gait-phase awareness into the mapping optimization process. Building upon the ORB-SLAM2 architecture, our methodology introduces two principal innovations: 1) a gait-synchronized pose prediction model that dynamically adjusts motion estimation parameters according to cyclic locomotion patterns and 2) an adaptive keyframe selection strategy that optimizes temporal sampling based on phase-dependent stability characteristics. By integrating real-time gait-phase detection with visual-inertial data fusion, our system demonstrates improved robustness against transient sensor perturbations and high-frequency vibrations inherent in dynamic locomotion. Quantitative evaluations reveal that this gait-adaptive optimization framework achieves superior tracking performance compared with conventional SLAM implementations, with lower absolute trajectory error, higher loop closure detection accuracy, and enhanced mapping consistency with less drift and overlap. The proposed architecture effectively decouples locomotion-induced sensor noise from true environmental observations, maintaining relatively high tracking precision even under severe gait disturbances (peak acceleration > 13m/s2). This advancement enables reliable long-term autonomous operation in unstructured environments where traditional vision-based SLAM systems typically suffer from error accumulation and mapping degradation.