Improving EKF-based solutions for SLAM problems in Mobile Robots employing Neuro-Fuzzy Supervision

2006 3rd International IEEE Conference Intelligent Systems Pub Date : 2006-09-01 DOI:10.1109/IS.2006.348502

A. Chatterjee, F. Matsuno

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引用次数: 12

Abstract

The determination of the solution for simultaneous localization and mapping (SLAM) problems has gained significant research momentum in recent times. Although extended Kalman filters have been extensively employed to solve these problems in mobile robots, the performance of the EKF can degrade significantly, if the correct a priori knowledge of process and sensor/measurement noise covariance matrices (Q and R respectively) is not available. Hence, the present paper proposes the development of a new robust solution method for SLAM problems where we employ a neuro-fuzzy system to supervise the performance of the EKF for SLAM problems and take necessary corrective actions by adapting the sensor statistics online, so that the degradation in system performance can be arrested. The free parameters of the neuro-fuzzy system are learned offline, by employing particle swarm optimization in the training phase. The system hence proposed is successfully evaluated by employing it to localize a mobile robot and simultaneously acquire the map of the environment, under benchmark environment situations with varying landmarks and with wrong knowledge of sensor statistics

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应用神经模糊监督改进基于ekf的移动机器人SLAM问题解决方案

同时定位与制图(SLAM)问题解决方案的确定是近年来研究的热点。虽然扩展卡尔曼滤波器已被广泛用于解决移动机器人中的这些问题，但如果没有正确的过程和传感器/测量噪声协方差矩阵(分别为Q和R)的先验知识，EKF的性能可能会显著下降。因此，本文提出了一种新的SLAM问题鲁棒求解方法的发展，其中我们使用神经模糊系统来监督SLAM问题的EKF的性能，并通过在线调整传感器统计量采取必要的纠正措施，从而可以阻止系统性能的退化。神经模糊系统的自由参数是离线学习的，在训练阶段采用粒子群算法。在具有不同地标和错误的传感器统计知识的基准环境情况下，通过使用该系统对移动机器人进行定位并同时获取环境地图，成功地对该系统进行了评估

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2006 3rd International IEEE Conference Intelligent Systems

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