Uma abordagem de alinhamento de minimapa usando informações de observação de pontos intrínsecos aos sistemas de SLAM Visual baseado em características

Abstract

This work proposes a map alignment approach based on a modification of the Iterative Closest Point (ICP) algorithm to consider point estimation confidence metrics already available in feature-based Visual SLAM systems. Mini-map alignment, in the context of a hierarchical map composed of several local representations of the environment, is an important task to allow the relationship of metric information between them, usually performed through the registration of the point clouds of each map. ICP is a widely used method in the literature for point cloud registration, but in the originally proposed form it does not consider the uncertainty of the points, and can be sensitive to noise, outliers and the initial estimate of the transformation. Feature-based Visual SLAM methods produce information intrinsic to the way they are modeled, which can represent the confidence of the map points and can be used to improve the alignment process. This research enumerates three possible SLAM metrics that can be used to represent the confidence of a map landmark, and investigates the potential of using these metrics to improve the ICP algorithm. The confidence metrics are incorporated into the ICP through a simple change in the correspondence estimation step to find the point with the highest confidence in a neighborhood of k nearest points. Experiments are conducted in different cases of initial misalignment to evaluate the influence of the confidence information suggested in this work, comparing the error of the final alignment of the point clouds and the number of iterations to achieve this alignment. The results show evidence that the use of confidence can help to improve the convergence of the ICP, both in the error of the final configuration and in the number of iterations required to achieve it.