High altitude monocular visual-inertial state estimation: Initialization and sensor fusion

Abstract

Obtaining reliable state estimates at high altitude but GPS-denied environments, such as between high-rise buildings or in the middle of deep canyons, is known to be challenging, due to the lack of direct distance measurements. Monocular visual-inertial systems provide a possible way to recover the metric distance through proper integration of visual and inertial measurements. However, the nonlinear optimization problem for state estimation suffers from poor numerical conditioning or even degeneration, due to difficulties in obtaining observations of visual features with sufficient parallax, and the excessive period of inertial measurement integration. In this paper, we propose a spline-based high altitude estimator initialization method for monocular visual-inertial navigation system (VINS) with special attention to the numerical issues. Our formulation takes only inertial measurements that contain sufficient excitation, and drops uninformative measurements such as those obtained during hovering. In addition, our method explicitly reduces the number of parameters to be estimated in order to achieve earlier convergence. Based on the initialization results, a complete closed-loop system is constructed for high altitude navigation. Extensive experiments are conducted to validate our approach.