Motorcycle trajectory reconstruction by integration of vision and MEMS accelerometers

MEMS accelerometers have the advantage with respect to traditional INS platforms of being miniaturized and economic. Cameras are, nowadays, also miniaturized and the necessity of broadcasting live video from on-board racing motorcycles solved problems such as the transmission of the video signal. The paper presents an algorithm for the accurate reconstruction of a motorcycle trajectory based on the integration of vision and MEMS accelerometers. In a previous paper it was shown that the images taken by the onboard camera on racing motorcycles were sufficient to roughly reconstruct the trajectory by model based estimation. A robust algorithm based on a cumulated Hough transform integrated in time with an appropriate dynamical model allowed for the reconstruction of the roll angle of the velocity and of an approximate trajectory of the motorcycle. Here, the algorithm is extended on one the hand to include measurements of accelerations and on the other hand to use visual landmarks to estimate biases and drifts of the dead reckoning sensors.