Illumination Robust Image Interval-Guided Adaptive Multifarious Hybrid Visual Inertial Odometry

Abstract

Illumination variations and rapid motion significantly impair the performance of visual inertial navigation systems (VINSs). Existing VINS algorithms struggle to achieve efficient and precise localization under conditions where both illumination variations and rapid motion occur simultaneously. To address this challenge, we propose an illumination robust image interval-guided adaptive multifarious hybrid visual inertial odometry (IRIH-VIO). Our approach begins with a metric-guided, multistage adaptive iterative image enhancement algorithm that processes environmental images with fluctuating lighting into a sequence of images with consistent and normalized lighting in real time, using only the CPU. The enhanced image sequence is then segmented into various intervals based on the contrast distribution of the original images. We subsequently perform adaptive switching and fusion of filtering and optimization in the outcomes of each interval. We introduce covariance inflation in the filtering stage to enhance sensitivity and resilience to rapid movements. Additionally, we developed a visual information weighting technique for feature extraction in the optimization process and incorporated it into the hybrid marginalization process. Experimental results from public datasets and real-world scenarios demonstrate that IRIH-VIO achieves superior performance in terms of accuracy and computational efficiency compared to state-of-the-art methods.