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

机译：用于踏板车运动和位置估计的低成本MEMS传感器和视觉系统

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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.
机译：在驾驶参考和最佳路线分析的映射参考系中准确地确定车辆位置的可能性是一个引起研究与开发部门极大兴趣的话题。为了达到精确的车辆定位和整合响应事件的目的，必须以高测量速率估算系统的位置，方向和速度。在这项工作中，我们测试了一种基于低成本传感器的系统，该系统基于微机电系统（MEMS）技术，并结合了来自安装在两轮机动车（踏板车）上的摄像机的信息。与四轮车相比；考虑到必须考虑更多的自由度，两轮车辆的动力学具有更高的复杂性。例如，一辆摩托车可以向侧面扭转。从而产生侧倾角。还必须考虑一个微小的俯仰角。与四轮汽车相比，轮悬架具有更高的运动度。在本文中，我们提出了一种精确重构“ Vespa”踏板车轨迹的方法。可以用作基于GPS / INS传感器集成的“经典”方法的替代方法。踏板车的位置和方向是通过卡尔曼滤波器和贝叶斯粒子滤波器的级联将基于MEMS的方向传感器数据与数字图像集成在一起而获得的。

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