Fusion of Low-Cost Imaging and Inertial Sensors for Navigation

Abstract

Aircraft navigation information (position, velocity, and attitude) can be determined using optical measurements from imaging sensors combined with an inertial navigation system. This can be accomplished by tracking the locations of optical features in multiple images and using the resulting geometry to estimate and remove inertial errors. A critical factor governing the performance of image-aided inertial navigation systems is the robustness of the feature tracking algorithm. Previous research has shown the strength of rigorously coupling the image and inertial sensors at the measurement level using a tactical-grade inertial sensor. While the tactical-grade inertial sensor is a reasonable choice for larger platforms, the greater physical size and cost of the sensor limits its use in smaller, low-cost platforms. In this paper, an image-aided inertial navigation algorithm is implemented using a multi-dimensional stochastic feature tracker. In contrast to previous research, the algorithms are specifically evaluated for operation using lowcost, CMOS imagers and MEMS inertial sensors. The performance of the resulting image-aided inertial navigation system is evaluated using Monte Carlo simulation and experimental data and compared to the performance using more expensive inertial sensors.