Efficient SLAM Algorithm Based on Quaternions for Real‐Time Navigation of Unmanned Aerial Vehicles

ABSTRACT Efficient quaternion‐based Simultaneous Localization and Mapping framework designed for real‐time navigation of Unmanned Aerial Vehicles in GPS‐denied environments is presented here. Unlike conventional Euler angle or matrix‐based SLAM approaches, the proposed system employs quaternion‐based propagation in Inertial Measurement Unit (IMU) module, thereby eliminating singularities such as gimbal lock and ensuring numerically stable orientation estimation. It integrates Quaternion Error‐State Extended Kalman Filter for incremental fusion of visual and inertial measurements and incorporates an incremental pose‐graph backend with quaternion retraction to reduce drift during long‐term navigation. Novelty lies in unifying quaternion mathematics with lightweight incremental optimization, thereby achieving a balance between accuracy and computational feasibility for real‐time onboard deployment. Experimental validation on KAIST Visual‐Inertial Odometry dataset demonstrates significant performance gains. Raw IMU is −50 Hz (20–25 ms/frame), far exceeding latency constraints of UAV flight dynamics. Additional analysis confirmed robustness with an average UAV speed of 1261.98 m/s, velocity stability (std. dev. 689.43 m/s) and reliable feature tracking (> 1800 map points reconstructed). These findings establish the framework as a novel, lightweight and robust SLAM solution suitable not only for UAVs but also for space–air–ground autonomous systems requiring accurate real‐time navigation under computational constraints.