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

2018 ◽  
Vol 16 (2) ◽  
pp. 868 ◽  
Author(s):  
A. Noordin ◽  
M. A. M. Basri ◽  
Z. Mohamed
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Attitude Estimation ◽  
Fusion Algorithm ◽  
Complementary Filter

scholarly journals Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Rhudy ◽  
Yu Gu ◽  
Jason Gross ◽  
Marcello R. Napolitano
Keyword(s):  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Square Root ◽  
Fusion Algorithm ◽  
Matrix Square Root ◽  
Global Positioning ◽  
The Matrix ◽  
Aerial Vehicle ◽  
Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

2016 ◽  
Vol 16 (18) ◽  
pp. 6997-7007 ◽  
Author(s):  
Jin Wu ◽  
Zebo Zhou ◽  
Jingjun Chen ◽  
Hassen Fourati ◽  
Rui Li
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Complementary Filter

Least square estimation-based adaptive complimentary filter for attitude estimation

2018 ◽  
Vol 41 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Parag Narkhede ◽  
Alex Noel Joseph Raj ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
Shashi Poddar
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Unmanned Vehicles ◽  
Least Square ◽  
Least Square Estimation ◽  
Micro Electro Mechanical Systems ◽  
Moving Vehicle ◽  
Complementary Filter ◽  
Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

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

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6752
Author(s):  
Lingxiao Zheng ◽  
Xingqun Zhan ◽  
Xin Zhang
Keyword(s):  
Inertial Sensors ◽  
Filter Design ◽  
Low Cost ◽  
Measurement Model ◽  
Attitude Estimation ◽  
System Stability ◽  
Feature Points ◽  
Complementary Filter ◽  
Geometry Measurement ◽  
Implicit Geometry

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.

scholarly journals A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Gu ◽  
Jason N. Gross ◽  
Matthew B. Rhudy ◽  
Kyle Lassak
Keyword(s):  
Sensor Fusion ◽  
Fault Tolerant ◽  
Attitude Estimation ◽  
Single Frequency ◽  
Test Bed ◽  
Multiple Sources ◽  
Fusion Algorithm ◽  
Cost Rate ◽  
Fault Detection And Identification ◽  
Multiple Sensor

A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.

3D Attitude Estimation in Indoor Environments with a Complementary Filter for the Special Orthogonal Group SO(3)

2013 ◽  
Vol 198 ◽  
pp. 153-158 ◽  
Author(s):  
Marcin Kmiecik ◽  
Krzysztof Sibilski
Keyword(s):  
Sensor Fusion ◽  
Orthogonal Group ◽  
Laser Scanner ◽  
Attitude Estimation ◽  
Practical Implementation ◽  
Indoor Environments ◽  
Test Bed ◽  
Complementary Filter ◽  
Satisfactory Quality

This paper presents a practical implementation of a complementary filter for 3D attitude estimation in an indoor setting. The structure of the filter is chosen basing on design principles described in [. It demonstrates the basic foundations of a complementary filter and proposes a simple and concise implementation for sensor fusion of data collected using an IMU and a Hokuyo URG 04LX laser scanner. Experimental results are shown for a handheld test bed satisfactory quality of estimation.

A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection

2017 ◽  
Author(s):  
Zhangjie Chen ◽  
Hanwei Liu ◽  
Yuqiao Wang ◽  
Ya Wang
Keyword(s):  
Sensor Fusion ◽  
Degrees Of Freedom ◽  
Learning Algorithm ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Activity Tracking ◽  
Term Care ◽  
Sensor Module

This paper presents a pan-tilt sensor fusion platform for activity tracking and fall-detection which can work as a reliable surveillance system with long-term care function. A low cost thermal array sensor and a distance sensor are integrated together as the sensor module. The sensor module is installed on a pan-tilt orienting mechanism with two rotation degrees of freedom to increase the field of view while reducing the number of sensors used on-board. The performance of the sensor test platform is analyzed. The location of the indoor object as well as its size can be estimated based on a novel sensor fusion algorithm. The support vector machine (SVM) based machine learning algorithm is applied for fall detection. The preliminary experiment result shows a 95% accuracy to identify falling action from similar normal indoor activity such as sitting and picking up stuff.

Attitude Estimation with Complementary Filter

2010 ◽  
Vol 44-47 ◽  
pp. 3781-3784
Author(s):  
Rui Hua Chang ◽  
Xiao Dong Mu ◽  
Xiao Wei Shen
Keyword(s):  
Solid State ◽  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Complementary Filter

An attitude estimation method is presented for a robot using low-cost solid-state inertial sensors. The attitude estimates are obtained from a complementary filter by combining the measurements from the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The results show that the estimation error is less than 1 degree compare to the reference attitude. It is a simple, yet effective method for attitude estimation, suitable for real-time implementation on a robot.

scholarly journals Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1937
Author(s):  
Parag Narkhede ◽  
Shashi Poddar ◽  
Rahee Walambe ◽  
George Ghinea ◽  
Ketan Kotecha
Keyword(s):  
Sensor Fusion ◽  
State Of The Art ◽  
Attitude Estimation ◽  
Fixed Frame ◽  
Complementary Filter ◽  
Nonlinear Version ◽  
Filter Architecture ◽  
Linear Version ◽  
Real World Datasets ◽  
Selection Of

Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.

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