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，因此需要对其进行估计。解决全局定位问题的现有方案，要么对噪声和环境任意性具有鲁棒性，但需要运动和耗时（而这可能或需要被节省），要么只需极短估计时间，但需假设环境结构、可能对噪声敏感，且需要预处理和参数调优。本文提出一种融合两类方法优势并规避其缺陷的新方法。该方法利用每射线累积绝对误差指标相对于二维LIDAR传感器位姿估计误差的特性，并通过扫描-地图-扫描匹配实现更精细的位姿近似。在真实与仿真条件下，涉及不同环境和传感器特性的大量测试表明，在姿态发现率与执行时间方面，所提方法优于两类解决方案中的现有最优方法。源代码可公开下载。