Error State Extended Kalman Filter Localization for Underground Mining Environments

Abstract

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

Energy efficient indoor wireless communication techniques based on BLE technology

“…A complete design of a multi-node, high-speed, low-power wireless communication system has significant practical value in real-world applications. In the development of portable devices, such as wearable devices, the majority of cases require a high-speed and feasible wireless communication system that meets the design requirements for data transmission [31][32][33][34][35]. In this paper, a feasible solution for such a multi-node, high-speed, low-power, shortrange communication system is presented, and corresponding validation and testing experiments are conducted.…”

A sensor fusion-based optimization method for indoor localization

“…To address the above-mentioned issues of localization error in TWDDM, this paper proposes a Local and Global combined Sensors Fusion (LGSF) method using both internal and external sensing information. An extended Kalman filter with internal sensors of wheel encoder and IMU (Inertial Measurement Unit) is fused to optimize the local pose estimation error between Odom coordinate and robot coordinate system [12]. And an Adaptive Monte Carlo Localization method with external sensor of LiDAR is implemented to get global positioning of the robot, ultimately optimize the relative error between map coordinate and odometer coordinate system and reduce odometry drift [13].…”

Sensor Pose Estimation and 3D Mapping for Crane Operations Using Sensors Attached to the Crane Boom

“…The EKF formulation and algorithm are well known for integrating diverse sensors in order to estimate the pose of the sensor [13], [23]- [26]. Here, we focus on conveying important implementation details.…”

“…The error state captures the accumulated modeling errors and process noise. We estimate this small error in the error state EKF and use it as a correction to the nominal state [28]. The error state vector is stated as…”