Department of Systems Engineering and Control, Instituto Universitario de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de Vera, E-46022 Valencia, Spain.
Sensors (Basel). 2013 Oct 21;13(10):14133-60. doi: 10.3390/s131014133.
This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.
本文提出了一种传感器融合框架，可利用有限的计算资源提高移动机器人的定位能力。它采用基于事件的卡尔曼滤波器，根据事件安排，在全球传感器和惯性测量单元（IMU）的测量值之间进行组合，使用较少的资源（执行时间和带宽），但与传统方法相比具有相似的性能。当估计误差协方差超过预定义限制时，事件被定义为反映全球信息的必要性。所提出的实验平台基于乐高 Mindstorm NXT，由一个带有天顶相机的差动轮式移动机器人和一个带有阿克曼转向的户外移动机器人组成，天顶相机作为全球传感器，而 SBG Systems GPS 通过 IGEP 板访问，该板还用作数据记录器。两个机器人中的 IMU 均使用 NXT 电机编码器以及一个来自 Hitecnic 的陀螺仪、一个指南针和两个加速度计构建，按照机器人的基于粒子的动态模型进行放置。所进行的测试反映了所提出框架的正确性能和低执行时间。在室内和室外环境中进行的长时间步行测试中观察到了其稳健性和稳定性。
