Autonomous map construction using three-dimensional feature descriptors

Autonomous robotic mapping has been an open research topic for more than twenty years. The primary objective of the robotic mapping problem is to design methods that can guide a robot around an environment and allow it to create a map of what has been sensed. Most automatic mapping algorithms rely on robot pose estimation to fuse map data together. This paper demonstrates that through feature extraction using spin-histograms, the pose of the robot can be estimated accurately enough for an iterative closest point (ICP) algorithm to register overlapping data sets. By eliminating consideration for points according to curvature and saliency, the spin-histogram matching process can improve in both accuracy and computation time. In combination with a global registration algorithm known as simultaneous matching, this process can obtain a fully autonomous registration process.