Navigation of a mobile robot is conditioned on the knowledge of its pose. In observer-based localisation configurations its initial pose may not be knowable in advance, leading to the need of its estimation. Solutions to the problem of global localisation are either robust against noise and environment arbitrariness but require motion and time, which may (need to) be economised on, or require minimal estimation time but assume environmental structure, may be sensitive to noise, and demand preprocessing and tuning. This article proposes a method that retains the strengths and avoids the weaknesses of the two approaches. The method leverages properties of the Cumulative Absolute Error per Ray (CAER) metric with respect to the errors of pose hypotheses of a 2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose estimations. A large number of tests, in real and simulated conditions, involving disparate environments and sensor properties, illustrate that the proposed method outperforms state-of-the-art methods of both classes of solutions in terms of pose discovery rate and execution time. The source code is available for download.

翻译：移动机器人的导航取决于对其位姿的认知。在基于观测器的定位配置中，其初始位姿可能无法预先获知，从而需要进行估计。针对全局定位问题的现有解决方案，要么对噪声和环境任意性具有鲁棒性，但需要运动和时间（而这些资源可能/需要被节省），要么估计时间极短，但假设了环境结构，可能对噪声敏感，并且需要预处理和参数调优。本文提出了一种方法，它保留了这两类方法各自的优点，同时避免了其缺点。该方法利用了累积绝对误差每射线（CAER）度量相对于二维激光雷达传感器位姿假设误差的特性，并利用扫描到地图扫描的匹配来进行精细（或更精细）的位姿估计。在真实与模拟条件下进行的大量测试，涉及不同的环境和传感器特性，结果表明所提方法在位姿发现率和执行时间方面均优于两类解决方案中的最先进方法。源代码可供下载。