A mini-map alignment approach using landmark observation information intrinsic to the feature-based visual SLAM pipeline

Abstract

This work proposes a map alignment approach based on a simple modification of the Iterative Closest Point algorithm to use point confidences based on metrics usually available in feature-based visual SLAM pipelines. In the context of a hierarchical map composed of mini-maps, aligning these mini-maps is an important task to allow metric information to be related between them. This research enumerates three possible SLAM metrics that could be used for representing landmark confidence, and investigate the potential of using these metrics to improve the ICP algorithm. The experiments show evidence that the usage of the confidence metrics might help to improve the convergence of ICP with only a small modification to the data association step.