Abstract
This paper studies the fusion of control inputs and IMU data for developing a kinematic bicycle motion model for the KTH smart mobility lab small-vehicles-for-autonomy (SVEA) platform. This motion model is filtered with relative pose estimates between a camera and fiducial markers, using both an extended Kalman filter and a particle filter. The developed motion models and filters are implemented on SVEA vehicles and are tested in the smart mobility lab. Pose estimates from the motion model and filters are compared against ground truth, determined by a motion capture system with sub-millimeter accuracy. The results presented provide the necessary base for development of automated vehicle control technologies on the SVEA platform with perception based on the detection of fiducial markers.
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Acknowledgements
All the equipment and setup to carry out the work were provided to us by the Integrated Transport Research Lab (ITRL), KTH Royal Institute of Technology. This paper was completed as part of the EL2320 Applied Estimation course at KTH, with feedback provided by Associate Professor John Folkesson. Additional support and assistance were provided by Frank Jiang and Tobias Bolin from KTH ITRL.
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Coble, K., Mahajan, A., Kaul, S., Singh, H.P. (2022). Motion Model and Filtering Techniques for Scaled Vehicle Localization with Fiducial Marker Detection. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_47
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