Error State Extended Kalman Filter Localization for Underground Mining Environments

The article addresses the issue of mobile robotic platform positioning in GNSS-denied environments real-time. proposed system relies on fusing data from an Inertial Measurement Unit (IMU), magnetometer, and encoders. To get symmetrical error gauss distribution for measurement model achieve better performance, Error-state Extended Kalman Filter (ES EKF) is chosen. There are two stages vector state determination: propagation based accelerometer gyroscope correction by measurements additional sensors. composed velocities along x y axes generated combining encoder orientation magnetometer around axis z. angle obtained directly. key feature algorithm IMU measurements’ isolation sensor data, with its further summation step. Validation performed a simulation ROS (Robot Operating System) Gazebo environment grounds developed mathematical model. Trajectories ES EKF, (EKF), Unscented (UKF) algorithms obtained. Absolute position errors all trajectories calculated EVO package. It shown that using simplified version IMU’s equations allows achievement comparable algorithm, EKF UKF.