Complementary Filter For Attitude Estimation Research Articles

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

An adaptive method of attitude and position estimation during GPS outages

Aiming to improve the accuracy of navigation systems during GPS outages, this paper presents an adaptive-gain complementary filter for attitude estimation. With the introduction of the acceleration vector as the observation, system dynamic information is considered to handle the high-frequency interference caused by external acceleration. Meanwhile, this paper presents a position prediction algorithm based on fuzzy neural networks with velocity and GPS position increment as the desired outputs. A hybrid method of the Least Mean Square (LMS) and conjugate gradient method is utilized to tune the parameters. With a 160 s non-overlapping sliding window, a flight test has been done using the proposed methods during 240 s GPS outages. The results indicate that the attitude estimation algorithm (RMSE of 0.89° and 0.45° for roll and pitch angles) performed better than the Mahony algorithm, and position prediction errors are 0.93 m and 1.12 m for latitude and longitude respectively.

Discrete-time complementary filter for attitude estimation based on MARG sensor

The MARG sensor, which stands for the combination of a magnetometer, an accelerometer, and a gyroscope, is widely used for 3D attitude measurement. Among the mainstream solutions for MARG-based attitude estimation, the complementary filter (CF) is normally regarded as a simplified alternative to the Kalman filter (KF), mainly because CF can reduce the amount of calculations. A dual-vector discrete-time CF (DV-DTCF) and its tuning methods are introduced in this paper. Different from the quaternion-based attitude estimation algorithms, DV-DTCF has a linear measurement model, since it utilizes the gravity and geomagnetic vectors as its state variables instead of quaternions. This feature of DV-DTCF can avoid linearization error or the use of nonlinear algorithms, and can also greatly reduce its computational complexity. More interestingly, it is analytically revealed, and experimentally proven, that the proposed DV-DTCF is fully equivalent to a fixed-gain KF. This fascinating fact leads straightforwardly to the tuning methods of DV-DTCF via the corresponding fixed-gain KF and Riccati equation. These tuning methods of DV-DTCF are based on the statistic characteristics of MARG sensor noise, and that makes them solid and feasible. According to experimental results, DV-DTCF can achieve the same accuracy as that of commonly-used KF algorithms in MARG-based attitude estimation, but with much lower time consumption. Hence, the proposed DV-DTCF is especially suitable for applications that have strict limitations on computational costs.

Complementary Filter for Attitude Estimation Based on MARG and Optical Flow Sensors

The combination of tri-axial magnetometer, accelerometer, and gyroscope has been widely used for three-dimensional attitude measurement, and this combination is also called MARG sensor. However, the measurement of accelerometer contains both the gravitational and motional acceleration, and only the former is useful for attitude estimation. As a result, MARG-based attitude estimation is easily disturbed by motion acceleration. In this paper, we introduce a complementary filter that estimates gravity and geomagnetic vectors in parallel, and utilize an optical flow sensor to detect and compensate motion acceleration. Experiment results show that the proposed algorithm has better performance than the existing ones when experiencing linear acceleration.

Attitude Adaptive Estimation With Smartphone Classification for Pedestrian Navigation

Accurate attitude for wearable devices and smartphones is needed for many applications. The major challenge is to cope with the acceleration resulting from the user or smartphone dynamics. To that end, a two-stage adaptive complementary filter for attitude estimation is proposed. Upon identifying the smartphone location on the user using a deep learning approach, the accelerometers weights in each axis are adjusted according to an optimized gain map. To evaluate the benefits of the proposed approach it is compared to commonly used algorithms both in simulation and experiments.

Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera.

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

Corrections to “Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors”

In the reference by the authors [1] , the quaternion fusion from accelerometer and magnetometer measurements does not have proper mathematical form. The presented equation (43) in [1] , actually takes a unit quaternion for attitude representation with a direction vector n followed by a scalar s , which reads [2]

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

Author Correction to: PSO Aided Adaptive Complementary Filter for Attitude Estimation

Author Correction to: PSO Aided Adaptive Complementary Filter for Attitude Estimation

Relaxing the planar assumption: 3D state estimation for an autonomous surface vessel

Autonomous Surface Vessels (ASVs) are increasingly being proposed as tools to automate environmental data collection, bathymetric mapping and shoreline monitoring. For many applications it can be assumed that the boat operates on a 2D plane. However, with the involvement of exteroceptive sensors like cameras or laser rangefinders, knowing the 3D pose of the boat becomes critical. In this paper, we formulate three different algorithms based on 3D extended Kalman filter state estimation for ASV localization. We compare them using field testing results with ground truth measurements, and demonstrate that the best performance is achieved with a model-based solution in combination with a complementary filter for attitude estimation. Furthermore, we present a parameter identification methodology and show that it also yields accurate results when used with inexpensive sensors. Finally, we present a long-term series (i.e. over a full year) of shoreline monitoring data sets and discuss the need for map maintenance routines based on a variant of the Iterative Closest Point algorithm.

Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots

This paper presents evaluation of four different state estimation architectures exploiting the extended Kalman filter (EKF) for 6-DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot's odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex-structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error (RPE) of distance driven) and implemented for real-time onboard processing during a rescue mission deployment.

Improved complementary filter for attitude estimation of micro air vehicles using low-cost inertial measurement units

Concerning the issue of how to achieve effective estimation of gravitational acceleration for attitude estimation of Micro Air Vehicle(MAV) under all dynamic conditions,an improved explicit complementary filter was proposed in combination with stepped-gain schedule.In order to validate the nonlinear complementary filter in the case when a MAV circled for an extended period,a centripetal acceleration mode was built using gyroscopes and indicated airspeed data,which resulted in precise estimation based on the estimation of gravitational acceleration and avoided reconstructing an estimation of the attitude.In the phase of Proportional-Integral(PI) compensation,the proportional gain and integral gain can achieve better adaptability by assigning different cut-off frequency value to the estimation of pitch and roll angles respectively.The experimental results show that the attitude angle estimation can be maintained under the range of ±2°.Compared with the typical filter algorithm,a better performance was achieved with respect to the efficiency and estimation error,so the algorithm proposed in this paper can be applied to accurate attitude estimation for MAV with low-cost inertial measurement units.

A Least Squares Estimate of Satellite Attitude

A Least Squares Estimate of Satellite Attitude