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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform
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Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

机译：用于机器人自定位的地图匹配算法：完美匹配，迭代最近点和正常分布变换之间的比较

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The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster.
机译：环境中移动机器人的自定位是机器人导航领域中最基本的问题之一。由于自主移动车辆的高要求，特别是关于算法精度，鲁棒性和计算效率，这是一个复杂和具有挑战性的问题。在本文中，我们展示了基于自然地标在本地化中应用的三种最常用的地图匹配算法的比较：我们实现了完美匹配（PM）和点云库（PCL）实现的迭代最近点（ ICP）和正态分布变换（NDT）。出于此比较的目的，我们考虑了一组代表性指标，例如姿势估计精度，计算效率，收敛速度，最大可允许初始化误差和机器人传感器中的异常值的鲁棒性。使用我们的ROS天然地标公共数据集检索测试结果，其中包含具有模拟和实际传感器数据的多个测试。完美匹配的性能和稳健性在本文中突出显示，对于具有有限的计算能力的实时嵌入式系统，对于需要准确的姿势估计和高速导航的快速反应时间，对于实时嵌入式系统至关重要。此外，我们将PCL添加到PCL一种用于使用由PM方法启发的查找表来执行对应估计来解决此问题的新算法。这种用于计算最近地图指向给定传感器读数的新方法被证明比PCL中的现有K-D树方法快40到60倍，并允许迭代最接近点算法更快地执行点云登记5到9倍。

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