scholarly journals Relative Position Estimation using Kalman Filter Based on Inertial Sensor Signals Considering Soft Tissue Artifacts of Human Body Segments

2020 ◽  
Vol 29 (4) ◽  
pp. 237-242
Author(s):  
Chang June Lee ◽  
Jung Keun Lee
Keyword(s):  
Kalman Filter ◽  
Soft Tissue ◽  
Human Body ◽  
Relative Position ◽  
Inertial Sensor ◽  
Position Estimation ◽  
Body Segments ◽  
Sensor Signals ◽  
Soft Tissue Artifacts

scholarly journals Obstacle Tracking on Unmanned Surface Vehicle Using Kalman Filter

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Rusdhianto Effendi Abdul Kadir ◽  
Mochammad Sahal ◽  
Yusuf Bilfaqih ◽  
Zulkifli Hidayat ◽  
Gaung Jagad
Keyword(s):  
Kalman Filter ◽  
System Design ◽  
Relative Position ◽  
Water Surface ◽  
Tracking System ◽  
Position Estimation ◽  
Guidance System ◽  
Light Detection And Ranging ◽  
Unmanned Surface Vehicle ◽  
Surrounding Environment

Unmanned Surface Vehicles (USV) are self-driving vehicles that operate on the water surface. In order to be operated autonomously, USV has a guidance system designed for path planning to reach its destination. The ability to detect obstacles in its paths is one of the important factors to plan a new path in order to avoid obstacles and reach its destination optimally. This research designed an obstacle tracking system which integrates USV perception sensors such as camera and Light Detection and Ranging (LiDaR) to gain information of the obstacle’s relative position in the surrounding environment to the ship. To improve the relative position estimation of the obstacles to the ship, Kalman filter is applied to reduce the measurements noises. The results of the system design are simulated using MATLAB software so that results can be analyzed to see the performance of the system design. Results obtained using the Kalman filter show 12% noise reduction. Keywords: filter kalman, obstacle tracking, unmanned surface vehicle.

scholarly journals Automatic identification of inertial sensor placement on human body segments during walking

2013 ◽  
Vol 10 (1) ◽  
pp. 31 ◽  
Author(s):  
Dirk Weenk ◽  
Bert-Jan F van Beijnum ◽  
Chris TM Baten ◽  
Hermie J Hermens ◽  
Peter H Veltink
Keyword(s):  
Human Body ◽  
Inertial Sensor ◽  
Sensor Placement ◽  
Automatic Identification ◽  
Body Segments

Development of a Reconfigurable Mobile Robot and Precise Position Estimation Based on Extended Kalman Filter

2006 ◽  
Vol 326-328 ◽  
pp. 1225-1228
Author(s):  
Gee Hwan Yeo ◽  
Jung Kim ◽  
Bong Soo Kang
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
High Reliability ◽  
Position Estimation ◽  
Experimental Results ◽  
Precise Position ◽  
Design Concepts ◽  
Multiple Sensors ◽  
Sensor Signals

This paper presents design concepts of a reconfiguration mobile robot developed in Hannam University and experimental results of position estimation by multiple sensors. In order to achieve high reliability and mobility in maneuver, driving motors of the mobile robot are assembled inside the wheels of the mobile robot, and the rhombus-shaped structure of the mobile robot with four wheels yields as good adaptability to rough terrain as a six-wheel mobile robot. Since the proposed mobile robot receives multiple sensor signals from odometers and an orientation sensor, states related to position and orientation of the mobile robot are optimally calculated by the extended Kalman filter. Experimental results show that tracking errors of the mobile robot can be reduced remarkably by the optimal state observer.

scholarly journals Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation

2017 ◽  
Vol 7 (5) ◽  
pp. 2596 ◽  
Author(s):  
Lasmadi Lasmadi ◽  
Adha Imam Cahyadi ◽  
Samiadji Herdjunanto ◽  
Risanuri Hidayat
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Filter Design ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Position Estimation ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Drift Compensation ◽  
Bias Compensation

The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.

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