Existing laser-based 2D simultaneous localization and mapping (SLAM) methods exhibit limitations with regard to either efficiency or map representation. An ideal method should estimate the map of the environment and the state of the robot quickly and accurately while providing a compact and dense map representation. In this study, we develop a new laser-based SLAM algorithm by redesigning the two core elements common to all SLAM systems, namely the state estimation and map construction. Utilizing Gaussian process (GP) regression, we propose a new type of map representation based on the regionalized GP map reconstruction algorithm. With this new map representation, both the state estimation method and the map update method can be completed with the use of concise mathematics. For small- or medium-scale scenarios, our method, consisting of only state estimation and map construction, demonstrates outstanding performance relative to traditional occupancy-grid-map-based approaches in both accuracy and especially efficiency. For large-scale scenarios, we extend our approach to a graph-based version.

中文翻译：

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GP-SLAM：基于区域高斯过程图重构的基于激光的SLAM方法

现有的基于激光的2D同时定位和制图（SLAM）方法在效率或地图表示方面都存在局限性。一种理想的方法应该快速，准确地估算环境图和机器人状态，同时提供紧凑而密集的地图表示形式。在这项研究中，我们通过重新设计所有SLAM系统共有的两个核心元素，即状态估计和地图构造，开发了一种基于激光的SLAM新算法。利用高斯过程（GP）回归，我们提出了一种基于区域化GP地图重构算法的新型地图表示。利用这种新的地图表示形式，可以使用简洁的数学方法完成状态估计方法和地图更新方法。对于中小型方案，我们的方法 仅由状态估计和地图构造组成，相对于传统的基于占用量地图的方法，在准确性和效率方面都表现出出色的性能。对于大型方案，我们将方法扩展到基于图的版本。