Intelligent Localization Using Vision and Inertial Sensor Fusion

Abstract

Autonomous mobile robots operating in dynamically changing, unstructured environments require high-precision, drift-free localization capabilities to achieve robust operational safety and navigational efficacy. While visual Simultaneous Localization and Mapping (vSLAM) and Inertial Navigation Systems (INS) serve as foundational technologies in intelligent automation, standalone implementations encounter significant vulnerabilities, specifically optical occlusion and cumulative dead-reckoning drift. This paper presents a comprehensive study on an intelligent, optimization-based, tightly-coupled vision-inertial sensor fusion framework designed for robust localization in challenging environments. The proposed system integrates high-frequency inertial measurements from an Inertial Measurement Unit (IMU) with high-fidelity visual landmarks extracted from a monocular camera, utilizing an artificial intelligence-driven adaptive Extended Kalman Filter (EKF) state estimation matrix to dynamically adjust measurement noise weights. By analyzing the structural characteristics of feature tracking alongside high-frequency acceleration profiles, the intelligent layer dampens sensor anomalies caused by aggressive motion or lightning fluctuations. Experimental validations conducted using the EuRoC MAV public benchmark dataset indicate that the proposed intelligent fusion architecture provides superior performance across dynamic trajectories, reducing the Absolute Trajectory Error (ATE) by up to 34% compared to classical loosely-coupled filtering methods while maintaining sub-centimeter positional drift thresholds.