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A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

机译：一种多创新，忘记基于因子的移动机器人的EKF-SLAM方法

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摘要

Purpose - The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity. Design/methodology/approach - A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations. Findings - The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping. Originality/value - The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.
机译：目的 - 本文的目的是探讨忘记基于因子的EKF-SLAM（FMI-EKF-SLAM）算法来解决误差问题的多创新，由违反本地线性的扩展卡尔曼滤波（EKF）引起的误差问题。由于强烈的非线性，移动机器人的同时定位和映射（SLAM）的假设。设计/方法/方法 - 调查了遗忘系数的EKF-SLAM（FMI-EKF-SLAM）算法的多创新。在每个过滤步骤中，FMI-EKF-SLAM算法将目前的步骤中的单一创新扩展到包含当前和上一步的扩展多重创新，并介绍了降低旧创新效果的遗忘因素。结果 - 仿真结果表明，探索的FMI-EKF-SLAM方法减少了状态估计误差，获得了理想的过滤效果，实现了更高的定位和映射精度。原创性/值 - 本文提出的方法提高了SLAM的定位精度并改善了EKF，使EKF具有更高的精度和更广泛的应用范围。

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