Low-cost MEMS Inertial Measurement Unit Research Articles

Low-Cost Attitude Estimation Using GPS/IMU Fusion Aided by Land Vehicle Model Constraints and Gravity-Based Angles

This paper details a method to improve accuracy of the land vehicle attitude estimation. It employs the vehicle model constraints to enhance the integration of GPS and a low-cost MEMS inertial measurement unit (MIMU) (GPS/MIMU). To improve the yaw angle estimation, we propose a lateral velocity constraint (LVC) aided method based on the observability analysis for the integrated fusion system. The theoretical analysis indicates that the yaw angle will be directly observed if LVC is augmented into the GPS/MIMU fusion algorithm. Furthermore, for LVC/MIMU system without GPS, the roll angle is always well estimated regardless of whether the vehicle is moving or stationary, and, surprisingly, the pitch angle can also be relatively accurately estimated if the vehicle is moving. Thus, when GPS is unavailable, the LVC/MIMU fusion is proposed by replacing the GPS measurement with the virtual measurement LVC. It is an encouraging improvement since the accumulating errors induced by gyro bias can be suppressed using MIMU alone. Moreover, to overcome the shortage that the pitch angle is not observed if the vehicle is stationary, we propose to fuse the gyros with gravity-based angles by Kalman filtering if the vehicle is detected motionless based on a switching rule. The experimental results compare favorably to theoretical analysis, showing that the proposed method can effectively improve the accuracy for land vehicle attitude estimation.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

SUMMARYLocalization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

Rapid and accurate initial alignment of the low-cost MEMS IMU chip dedicated for tilted RTK receiver

The new global navigation satellite system real-time kinematic (RTK) receivers with tilt compensation extend the applicability of high-precision RTK positioning to previously restrictive environments since the pole is no longer required to be maintained vertically. The rapid and accurate initial alignment of the built-in low-cost inertial measurement unit (IMU) is a critical technical issue and currently remains a challenge for the tilted RTK. We address this problem by proposing a method specific to the determination of the IMU initial heading for the tilted RTK. In this approach, the IMU heading is calculated from the angle between the inertial navigation system (INS)- and RTK-indicated position increment vectors in the horizontal plane based on the fact that the INS- and RTK-indicated trajectories in the short term are similar in shape. Instead of conventional INS mechanization, the INS-indicated trajectory is determined using the gyro-derived attitude and pole length by treating the pole movement as the motion of a rigid body with a fixed point to enhance the trajectory accuracy. The experiment results indicate that the initial heading is accurately determined to 1.15° at a 98.2% confidence level within only 2–3 s when using a dollar-level IMU chip without any additional sensors, e.g., a magnetometer. The proposed algorithm satisfactorily meets the requirements of the tilted RTK application, and its accuracy and convergence speed are higher than those of the current methods reported in publications and product sheets.

Adaptive Attitude Estimation for Low-Cost MEMS IMU Using Ellipsoidal Method

In this article, the attitude estimation for low-cost MEMS inertial measurement units in a smartphone is proposed using adaptive ellipsoidal methods. Accelerometer and magnetometer measurements in an attitude heading reference system (AHRS) are ideally applicable when there is no acceleration and magnetic disturbance. However, when the AHRS is applied to the smartphone, acceleration is frequently occurred by hand movement, and it causes significant attitude errors. Besides, magnetic disturbance from the surrounding environment degrades the heading estimation. In order to improve the attitude accuracy, the acceleration from movement, in addition to the magnetic disturbance is considered in adaptive logic using measurement residuals in this article. The performance of the proposed algorithm is simulated by the computer and tested using the rate table and optical motion capture system compared with the conventional algorithms.

Development of an Attitude Determination System for Mobile Robots

This paper designs an attitude determination system based on a low-cost MEMS inertial measurement unit. By introducing an adaptive Kalman filter algorithm, the outputs of accelerometers, magnetometers, and gyroscopes are combined to solve the robot’s attitude angle in real time. The algorithm effectively eliminates the cumulative error of the gyro output. In addition, the output of the accelerometer is used to determine the motion state of the vehicle body, and the covariance matrix is adaptively adjusted to maintain a high-accuracy attitude output in both static and dynamic environments.

Low Cost Integrated Navigation System for Unmanned Vessel

Abstract Large errors of low-cost MEMS inertial measurement unit (MIMU) lead to huge navigation errors, even wrong navigation information. An integrated navigation system for unmanned vessel is proposed. It consists of a low-cost MIMU and Doppler velocity sonar (DVS). This paper presents an integrated navigation method, to improve the performance of navigation system. The integrated navigation system is tested using simulation and semi-physical simulation experiments, whose results show that attitude, velocity and position accuracy has improved awfully, giving exactly accurate navigation results. By means of the combination of low-cost MIMU and DVS, the proposed system is able to overcome fast drift problems of the low cost IMU.

Accurate Attitude Estimation of a Moving Land Vehicle Using Low-Cost MEMS IMU Sensors

This paper presents a novel Kalman filter for the accurate determination of a vehicle’s attitude (pitch and roll angles) using a low-cost MEMS inertial measurement unit (IMU) sensor, comprising a tri-axial gyroscope and a tri-axial accelerometer. Currently, vehicles deploy expensive gyroscopes for attitude determination. A low-cost MEMS gyro cannot be used because of the drift problem. Typically, an accelerometer is used to correct this drift by measuring the attitude from gravitational acceleration. This is, however, not possible in vehicular applications, because accelerometer measurements are corrupted by external accelerations produced due to vehicle movements. In this paper, we show that vehicle kinematics allow the removal of external accelerations from the lateral and vertical axis accelerometer measurements, thus giving the correct estimate of lateral and vertical axis gravitational accelerations. An estimate of the longitudinal axis gravitational acceleration can then be obtained by using the vector norm property of gravitational acceleration. A Kalman filter is designed, which implements the proposed solution and uses the accelerometer in conjunction with the gyroscope to accurately determine the attitude of a vehicle. Hence, this paper enables the use of extremely low-cost MEMS IMU for accurate attitude determination in vehicular domain for the first time. The proposed filter was tested by both simulations and experiments under various dynamic conditions and results were compared with five existing methods from the literature. The proposed filter was able to maintain sub-degree estimation accuracy even under very severe and prolonged dynamic conditions. To signify the importance of the achieved accuracy in determining accurate attitude, we investigated its use in two vehicular applications: vehicle yaw estimate and vehicle location estimate by dead reckoning and showed the performance improvements obtained by the proposed filter.

Heading Estimation with Real-time Compensation Based on Kalman Filter Algorithm for an Indoor Positioning System

The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning) algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.

Towards Low - Cost Mems Imu Gait Analysis: Improvements Using Calibration and State Estimation

Inexpensive, unobtrusive 3D motion tracking of human gait is of increasing interest for the medical and entertainment industries. Of particular interest are rehabilitative applications. For instance, being able to measure foot travel, e.g. stride length or foot clearance, would be very useful. Approaches using low-cost MEMS inertial measurement units have often been limited by requiring expensive calibration procedures and by the sensor’s inherent noise and bias drift. The authors apply two techniques to improve IMU based gait tracking: a novel calibration routine and a zero-velocity bias update algorithm. The application of these aids reduces error by an average of 99.55% over six trails. Results show a 5.96% tracking accuracy in the progressive direction, which corresponds to errors on the centimeter scale.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

무인 비행체용 저가의 ADGPS/INS 통합 항법 시스템

An unmanned aerial vehicle (UAV) is an aircraft controlled by .emote commands from ground station and/o. pre-programmed onboard autopilot system. A navigation system in the UAV provides a navigation data for a flight control computer(FCC). The FCC requires accurate and reliable position, velocity and attitude information for guidance and control. This paper proposes an ADGPS/INS integrated navigation system for a UAV. The proposed navigation system comprises an attitude determination GPS (ADGPS) receive., a navigation computer unit, and a low-cost commercial MEMS inertial measurement unit(IMU). The navigation algorithm contains a fault detection and isolation (FDI) function fur integrity. In order to evaluate the performance of the proposed navigation system, two flight tests were preformed using a small aircraft. The first flight test was carried out to confirm fundamental operation of the proposed navigation system and to check the effectiveness of the FDI algorithm. In the second flight test, the navigation performance and the benefit of the GPS attitude information were checked in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation system gives a reliable performance even when anomalous GPS data is provided and better navigation performance than a conventional GPS/INS integration unit.

System identification and fault accommodation for thruster propelled UUVs

This paper describes the current state of the work done at The University of Wales College Newport under the ‘improve the performance of remotely operated vehicles’ programmme (IMPROVES). The work incorporates advances in two interrelated parts of the project: it presents a technique to extract velocity from a low-cost MEMS inertial measurement unit for system identification and development of a combined control allocation and fault detection technique. The paper presents theoretical developments and results obtained on experimental trials.