Simultaneous localization and mapping method in three-dimensional space based on the combined solution of the point–point variation problem ICP for an affine transformation

Simultaneous localization and mapping is a problem in which frame data are used as the only source of external information to define the position of a moving camera in space and at the same time, to reconstruct a map of the study area. Nowadays, this problem is considered solved for the construction of two-dimensional maps for small static scenes using range sensors such as lasers or sonar. However, for dynamic, complex, and large-scale scenes, the construction of an accurate three-dimensional map of the surrounding space is an active area of research. To solve this problem, the authors propose a solution of the point–point problem for an affine transformation and develop a fast iterative algorithm for point clouds registering in three-dimensional space. The performance and computational complexity of the proposed method are presented and discussed by an example of reference data. The results can be applied for navigation tasks of a mobile robot in real-time.