TY  - JOUR
T1  - Real Time Noise Filtering for Low Cost IMU Sensors
AU - Das, Diganta AU - Roseline Mary, R. 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 6
SP  - 536
EP  - 543
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.536.543
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.536.543
KW  - Complementary filter
KW  -Kalman filter
KW  -quadcopter
KW  -accelerometer
KW  -gyroscope inertial measurement unit
KW  -noise filtering
KW  -arduino
KW  -embedded sensors
AB  - A low cost Inertial Measurement Unit (IMU) sensor typically includes a gyroscope and an
accelerometer with six degrees of freedom. Due to its low manufacturing cost, it is used in many small projects.
However, the raw data from these sensors are not completely reliable as the accelerometer generates a lot of
noise from physical vibrations and the gyroscope tends to produce a drift over time. Special filters such as
complementary filter and Kalman filter are commonly used to reduce the noise in real-time. For simple
applications complementary filter is fine but more complex and precise projects such as self-balancing robots
and quadcopters requires the use of the Kalman filter. The Kalman filter algorithm is quite complex and has a
lot of floating point matrix multiplications which can be very heavy for a small microprocessor such as arduino.
In applications like quadcopter the Proportional Integral Derivative (PID) loop has to run at minimum 80 Hz. This
research aims to develop a light weight modified version of Kalman filter which can be easily included as an
external library. This allows fast looping time for the microprocessor. The primary purpose of this library is for
applications which require low latency such as quadcopter and self-balancing robots but the library may prove
to be useful in other similar projects too.
ER  -