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Open AccessArticle

Low-Cost MEMS Sensors and Vision System for Motion and Position Estimation of a Scooter

CIRGEO, Interdepartment Research Center for Geomatics, University of Padova, viale dell'Università 16, 35020 Legnaro, Padova, Italy
Author to whom correspondence should be addressed.
Sensors 2013, 13(2), 1510-1522;
Received: 7 December 2012 / Revised: 18 January 2013 / Accepted: 21 January 2013 / Published: 24 January 2013
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter. View Full-Text
Keywords: Bayesian particle filter; Kalman filter; MEMS; Whippel model; motorcycle Bayesian particle filter; Kalman filter; MEMS; Whippel model; motorcycle
MDPI and ACS Style

Guarnieri, A.; Pirotti, F.; Vettore, A. Low-Cost MEMS Sensors and Vision System for Motion and Position Estimation of a Scooter. Sensors 2013, 13, 1510-1522.

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