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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots
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Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots

机译：基于EKF的估计模型的评估，以用于移动机器人中的数据融合

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摘要

This paper presents evaluation of four different state estimation architectures exploiting the extended Kalman filter (EKF) for 6-DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot's odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex-structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error (RPE) of distance driven) and implemented for real-time onboard processing during a rescue mission deployment.
机译：本文介绍了利用扩展卡尔曼滤波器（EKF）进行移动机器人6自由度推算的四种不同状态估计架构的评估。 EKF是将惯性数据与机器人的里程表融合的一种久经考验且常用的技术。但是，设计状态估计器体系结构的不同方法会导致不同的性能和计算需求。在为移动机器人寻求最佳解决方案的同时，解决了非线性模型和误差模型，无论有无辅助姿态估计滤波器。该性能是通过室内和室外导航精度（包括楼梯和崎terrain地形等复杂结构环境）的精度实验确定的。根据评估，非线性模型与互补滤波器相结合被选为最佳候选模型（达到0.8m RMSE和距离驱动的4％返回位置误差（RPE）的平均值），并在救援过程中实现了实时车载处理任务部署。

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