TY - GEN
T1 - Automotive Dead Reckoning-Implementation of Fusion Algorithm on Raspberry Pi
AU - Parodkar, Manjusha R.
AU - Sheta, Milankumar
AU - Chokkadi, Shreesha
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - This manuscript considers the problem of position and heading estimation of autonomous vehicles in situations where the vehicle’s global positioning system (GPS) is assumed to be not accessible. The position and heading estimation has been done with the use of inertial measurement unit (IMU) and vehicle dynamical models in a Raspberry Pi environment. Suitable Kalman filter algorithm has been developed to prepare IMU data for fusion, and data from the GPS module has been also effectively decoded for use in the algorithm. An extended Kalman filter algorithm has been developed and implemented for two separate cases, one of which is where the vehicle is traveling on a straight road at constant speed, and the other is a vehicle moving on a curved road at constant speed. Both cases have been tested with and without GPS outages. The sensors are first calibrated and modeled to use in the filtering process, and the model has been tested in the simulation environment created with CarMaker.
AB - This manuscript considers the problem of position and heading estimation of autonomous vehicles in situations where the vehicle’s global positioning system (GPS) is assumed to be not accessible. The position and heading estimation has been done with the use of inertial measurement unit (IMU) and vehicle dynamical models in a Raspberry Pi environment. Suitable Kalman filter algorithm has been developed to prepare IMU data for fusion, and data from the GPS module has been also effectively decoded for use in the algorithm. An extended Kalman filter algorithm has been developed and implemented for two separate cases, one of which is where the vehicle is traveling on a straight road at constant speed, and the other is a vehicle moving on a curved road at constant speed. Both cases have been tested with and without GPS outages. The sensors are first calibrated and modeled to use in the filtering process, and the model has been tested in the simulation environment created with CarMaker.
UR - https://www.scopus.com/pages/publications/85108902632
UR - https://www.scopus.com/pages/publications/85108902632#tab=citedBy
U2 - 10.1007/978-981-16-0336-5_17
DO - 10.1007/978-981-16-0336-5_17
M3 - Conference contribution
AN - SCOPUS:85108902632
SN - 9789811603358
T3 - Lecture Notes in Electrical Engineering
SP - 189
EP - 209
BT - Smart Sensors Measurements and Instrumentation - Select Proceedings of CISCON 2020
A2 - K V, Santhosh
A2 - Rao, K. Guruprasad
PB - Springer Science and Business Media Deutschland GmbH
ER -