Design, Development, and Deployment of Real-Time Sensor Fusion (CnW + EKF) for a Linux-Based Embedded System Using Qt-Anywhere

Abstract

This paper describes the design, development, and implementation of a real-time sensor fusion system that utilizes the classification and weighing plus extended Kalman filter algorithm to derive heading for navigation using inexpensive sensors. This algorithm was previously tested only through postprocessing using MATLAB and is now reprogrammed using Qt and deployed on a Linux-based embedded board for real-time operation. Various data from inexpensive sensors such as global positioning system devices, an electronic compass, and an inertial measurement unit were utilized to ultimately derive a more reliable and accurate heading value. The algorithm flow can be described with the GPS values first being evaluated and classified which are then fused with the EC heading using classification and weighing, whose result is then passed through an EKF to fuse with the IMU data. Real-time tests and trials were done to prove the operational capability of the developed process. The complete setup and configuration processes of the systems for development and deployment via Qt are also provided for those interested to replicate the process.

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

“…The measurements for estimating the state are taken from three GPS devices and one EC. The covariance for process noise (Q) and measurement noise (R) was set dependent The third stage was developed to address the difficulty in deploying to an embedded board the previously proposed algorithm that utilized the Covariance Intersection algorithm [39,40]. This stage fused the heading values from the multiple EKF processes with their corresponding weight values from the first stage (CnW-S1).…”

“…The multithreading capability of the desktop and target BBB-embedded board was utilized in order to simultaneously implement the fusion processes as well as polling data from the individual sensor devices. The complete sensor fusion was successfully deployed on an embedded board following the general steps given in our previous work [40] with additional focus on persistent naming to address the problem of device name reassignment in the Linux system.…”

“…This algorithm is based on our previously proposed work that was checked through postprocessing [39]. The system was developed using Qt-anywhere on a desktop with an Ubuntu Linux system and then deployed onto an Ubuntu Linux-based embedded board using another previous work of ours [40]. The proposed algorithm was tested and validated to generate a more accurate heading value compared to the individual GPS COG values and is even comparable to the more expensive Furuno Satellite Compass (GPS Compass) Model SC-50.…”