scholarly journals Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

Sadhana ◽  
2004 ◽  
Vol 29 (2) ◽  
pp. 217-235 ◽  
Author(s):  
N. Shantha Kumar ◽  
T. Jann
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Low Cost ◽  
Fusion Algorithm

scholarly journals Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sondre Sanden Tørdal ◽  
Geir Hovland
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Real Life ◽  
Fusion Algorithm ◽  
Six Degrees Of Freedom ◽  
Motion Estimate

In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

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.

A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic

2016 ◽  
Vol 04 (04) ◽  
pp. 245-254
Author(s):  
Akshay Rao ◽  
Wang Han ◽  
P. G. C. N. Senarathne
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Fusion Algorithm ◽  
Central Difference ◽  
Localization Algorithms ◽  
Kolmogorov Smirnov ◽  
Prediction Density ◽  
Smirnov Statistic

Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

scholarly journals Enhanced location tracking in sensor fusion-assisted virtual reality micro-manipulation environments

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261933
Author(s):  
John David Prieto Prada ◽  
Jintaek Im ◽  
Hyondong Oh ◽  
Cheol Song
Keyword(s):  
Virtual Reality ◽  
Kalman Filter ◽  
Sensor Fusion ◽  
Human Subjects ◽  
Great Accuracy ◽  
Small Scale ◽  
Location Tracking ◽  
Fusion Algorithm ◽  
Measuring Device ◽  
Positioning Errors

Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.

scholarly journals Sensor Fusion Algorithm Using a Model-Based Kalman Filter for the Position and Attitude Estimation of Precision Aerial Delivery Systems

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5227
Author(s):  
Raul A. Garcia-Huerta ◽  
Luis E. González-Jiménez ◽  
Ivan E. Villalon-Turrubiates
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Linear Models ◽  
Flight Dynamics ◽  
Delivery Systems ◽  
Fusion Algorithm ◽  
Model Based ◽  
Wide Range ◽  
Estimation Scheme ◽  
Double Integrator

In this research, we focus on the use of Unmanned Aerial Vehicles (UAVs) for the delivery of payloads and navigation towards safe-landing zones, specifically on the modeling of flight dynamics of lightweight vehicles denoted Precision Aerial Delivery Systems (PADSs). While a wide range of nonlinear models has been developed and tested on high-end applications considering various degrees of freedom (DOF), linear models suitable for low-cost applications have not been explored thoroughly. In this study, we propose and compare two linear models, a linearized version of a 6-DOF model specifically developed for micro-lightweight systems, and an alternative model based on a double integrator. Both linear models are implemented with a sensor fusion algorithm using a Kalman filter to estimate the position and attitude of PADSs, and their performance is compared to a nonlinear 6-DOF model. Simulation results demonstrate that both models, when incorporated into a Kalman filter estimation scheme, can determine the flight dynamics of PADSs during smooth flights. While it is validated that the double integrator model can adequately operate under the proposed estimation scheme for up to small acceleration changes, the linearized model proves to be capable of reproducing the nonlinear model characteristics even during moderately steep turns.

A Low-Cost MEMS Implementation Based on Sensor Fusion Algorithms

2015 ◽  
Vol 738-739 ◽  
pp. 42-45
Author(s):  
Xian Wei Wang ◽  
Jun Hai Jiang
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Output Signal ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
6 Degrees Of Freedom

In this paper a low-cost Micro-Electro-Mechanical System (MEMS) inertial measurement unit is designed, a 3-axis accelerometer and 3-axis gyroscope simulated 6 degrees of freedom orientation sensing through sensor fusion. By analyzing a simple complimentary filter and a more complex Kalman filter, the outputs of each sensor were combined and took advantage of the benefits of both sensors to improved results. The experimental results demonstrate that the output signal can be corrected suitability by means of the proposed method.

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