GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction

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.