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An improved particle filter for mobile robot localization based on particle swarm optimization

机译：基于粒子群算法的移动机器人定位改进粒子滤波器

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摘要

As one of the most important issues in the field of mobile robotics, self-localization allows a mobile robot to identify and keep track of its own position and orientation as the robot moves through the environment. In this work, a hybrid localization approach based on the particle filter and particle swarm optimization algorithm is presented, focusing on the localization tasks when an a priori environment map is available. This results an accurate and robust particle filter based localization algorithm that is able to work in symmetrical environments. The performance of the proposed approach has been evaluated for indoor robot localization and compared with two benchmark algorithms. The experimental results show that the proposed method achieves robust and accurate positioning results in indoor environments, requiring fewer particles than the benchmark methods. This advance could be integrated in a wide range of mobile robot systems, helping to reduce the computational cost and improve the navigation efficiency. (C) 2019 Elsevier Ltd. All rights reserved.
机译：作为移动机器人领域最重要的问题之一，自定位使移动机器人可以在机器人穿越环境时识别并跟踪自己的位置和方向。在这项工作中，提出了一种基于粒子滤波和粒子群优化算法的混合定位方法，着重于先验环境图可用时的定位任务。这将导致基于精确和鲁棒的粒子滤波器的定位算法能够在对称环境中工作。已针对室内机器人定位评估了所提出方法的性能，并与两种基准算法进行了比较。实验结果表明，所提出的方法在室内环境下可以获得鲁棒且准确的定位结果，所需的粒子数量少于基准方法。这一进步可以集成到各种移动机器人系统中，从而有助于降低计算成本并提高导航效率。 （C）2019 Elsevier Ltd.保留所有权利。

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