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 metric with respect to the errors of pose estimates of a 2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose approximations. 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：移动机器人的导航依赖于对其位姿的认知。在基于观测器的定位配置中，其初始位姿可能无法事先获知，因此需要对其进行估计。解决全局定位问题的方法要么对环境噪声和任意性具有鲁棒性，但需要运动和耗时（这些可能需要节省）；要么需要最小的估计时间，但假设环境结构已知，可能对噪声敏感，并需要预处理和参数调整。本文提出了一种方法，既能保留两种方法的优势，又能避免其缺陷。该方法利用了逐射线累积绝对误差度量相对于2D LIDAR传感器位姿估计误差的特性，并通过扫描与地图扫描匹配来实现更精确的位姿近似。在真实和模拟条件下进行的大量测试（涉及不同环境及传感器特性）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新方法。源代码可供下载。