Using n-grams of spatial densities to construct maps
Renan Maffei, Vitor A. M. Jorge, Vitor F. Rey, Guilherme S. Franco, Mariane Giambastiani, Jessica Barbosa, Mariana Kolberg, Edson Prestes
Abstract
Place recognition is the frond-end of Simultaneous Localization and Mapping (SLAM). Topological representations depend on good association of vertices, which ultimately depends on the front-end. In this paper, we consider a robot lost in an unknown environment trying to construct a topological map to localize itself using a laser range finder and odometry information. The algorithm makes use of an efficient observation model based on kernel density estimates (KDEs) to detect loops. The observation model separates the map into regions denominated words, classified based on the density of free space, number of observations and segment orientation. Loop closing results from the matching of sequences of N consecutive words (n-grams). The proposed approach is orders of magnitude faster than a sequence of Iterative Closest Point (ICP) matches. The method is evaluated varying input parameters in real and simulated scenarios.
BibTeX
@inproceedings{iros2015_usingngramsofspa,
title = {Using n-grams of spatial densities to construct maps},
author = {Renan Maffei and Vitor A. M. Jorge and Vitor F. Rey and Guilherme S. Franco and Mariane Giambastiani and Jessica Barbosa and Mariana Kolberg and Edson Prestes},
booktitle = {IROS 2015},
year = {2015}
}