Exposure-Midpoint Temporal Alignment and Risk-Aware Adaptive Feature Tracking for Stereo Visual–Inertial Odometry

Abstract

Visual–inertial odometry (VIO) on industrial stereo camera–IMU platforms suffers from frame-dependent timing errors and degraded feature tracking under auto-exposure, auto-gain adjustment, and rapid motion. This paper presents Hardware-Synchronized Exposure-State-Aware VINS-Fusion (HS-ES-VINS), a hardware-synchronized, exposure-aware extension of VINS-Fusion. The effective sampling time of each global-shutter image is reconstructed at the exposure midpoint using synchronized trigger timestamps, the camera debounce delay, and the frame-wise exposure duration. An exposure–gain–motion risk model adaptively adjusts feature detection criteria, Kanade–Lucas–Tomasi (KLT) tracking parameters, and correspondence filtering, while IMU-integrated rotation supplies initial predictions for feature tracking. On hardware-synchronized indoor and outdoor datasets with independent ground truth, controlled experiments show that the exposure-midpoint timestamp removes the frame-dependent exposure component of the camera–IMU offset that neither fixed nor online scalar compensation can remove, reducing the ATE RMSE by 28.6% (fixed offset) and 22.7% (online estimation) relative to the exposure-start timestamp under 20 ms low-light exposure. Compared with baseline VINS-Fusion, the complete adaptive front end reduces the ATE RMSE by 16.8% and 12.9% on indoor multi-floor and outdoor cycling sequences, and the risk score explains frame-wise tracking quality (correlation of 0.587 with the inlier ratio). The front end runs faster than the baseline (17.1 ms per frame), confirming real-time operation and improved robustness against variable illumination and rapid motion.