Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion

This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Cao

2020

“…For this, it is necessary to estimate the orientation error that exists in different sensors. A comparison of the orientation from different sensory systems is made with the IMU sensor (VN-100), since it is a highly accurate AHRS sensor [41]. The Root Mean Square Error (RMSE) is calculated by taking IMU VN-100 orientation angle as a reference, according to Equation (14):…”

Section: Dynamic Testsmentioning

confidence: 99%

“…Figure 21 below is a plot of the error in orientation angles. This plot is obtained by differencing the angle values between reference the AHRS (VN-100) and the designed algorithms [41], i.e., camera-based angle, CF, and KF. It is observed that the CF has some high jumps when the robot is rapidly turning clockwise or counterclockwise, whereas the KF faithfully tracks the rotation angle of the Mantis robot.…”

Section: Dynamic Testsmentioning

confidence: 99%

Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot

Vega-Heredia

Ilyas

Ghanta

et al. 2020

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

“…When these algorithms are used in ARS under vehicle movement conditions, the attitude estimate accuracy would be influenced by the MEMS accelerometer measurement errors [10]. In practice, multiple applications use complementary filters by fusing the output of a MEMS gyroscope and a MEMS accelerometer [11,12,13,14,15,16]. In earlier research, the attitude estimation system for an autonomous helicopter used a complementary filter obtained by fusing the output of a gyroscope and an inclinometer [15].…”

Section: Introductionmentioning

confidence: 99%

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

Xing

Hang

Xiong

et al. 2016

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.