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-Pujan Shrestha Class of 2015
Smartphone Inertial
Navigation
INTRODUCTION• Inertial navigation system (INS) use accelerometers, gyroscopes to
measure the position, orientation and velocity of a moving body.
• The calculation of the position and velocity is entirely Internal.
• Conventional accelerometers and gyroscopes have been increasingly replaced by microchips
WHAT AM I TALKING ABOUT?• Accelerometer is used to measure
proper acceleration.
• Gyroscopes measure the change in orientations.
OBJECTIVES• Develop an inertial Navigation system that can accurately calculate the
motion using a Smartphone.
• Develop and use Kalman Filter to filter data
• Contribute to the ongoing Roller Coaster research project.
METHODS• Use smartphone to take acceleration and gyroscope measurements from
different types of motion
• Take videos of the motion to perform video analysis in order to estimate true position
• Use Matlab to filter data to remove bias and noise in raw acceleration data
• Numerically integrate two times in order to convert acceleration data into velocity and velocity into position data
ANALYSIS PROCESS• Apply force onto a resting cart and record its motion• Filter the data obtained
Raw Acceleration Data Filtered Acceleration Data
• Capture the motion of the cart in a video camera and perform video analysis
• Numerically Integrate filtered acceleration data to get velocity
• Numerically Integrate velocity to obtain position and compare it to true position obtained from video analysis
RESULTS FROM OTHER TYPES OF MOTION
1D Complex motion
2D Simple Motion
2D Incline
Vertical Rotation
Horizontal Rotation
KALMAN FILTER• Recursive system consisting of a measurement update step and
prediction step.
• Uses the equations of motion, known control inputs and
measurements from the sensors to predict and calculate the various variables associated to the system.
• Could not be implemented because of the lack of known control variables in the motions
CONCLUSIONS
• Due to the nature of the motions experimented on, Kalman filter could not be applied onto it.
• The smartphone INS is accurate till 12-15 seconds and quadratic Drift was prevalent after that.
• More research needs to be done with newer smartphones in order to increase accuracy of the data
FUTURE WORK• Experiment on more complex motions like twists and turns
• Use sources of known control variables to attempt the Kalman filter
• Experiment using new smartphones from different vendors
• Develop an INS app for the phone.
• Continue Roller Coaster Research Project
ACKNOWLEDGEMENTS• Randolph College Summer Research Program• Dr. Peter Sheldon• Kacey Meaker ‘08• Alex Tran ‘15• Timothy Slesinger ‘14• Mike Cheng, Virginia Episcopal School• Richard Lin, Virginia Episcopal School