Improving Localisation Accuracy using Submaps in warehouses

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Daniel Adolfsson, Stephanie Lowry, and Henrik Andreasson
Improving Localisation Accuracy using Submaps in warehouses
IROS 2018: Workshop on Robotics for Logistics in Warehouses and Environments Shared with Humans.

Abstract

This paper presents a method for localisation in hybrid metric-topological maps built using only local information that is, only measurements that were captured by the robot when it was in a nearby location. The motivation is that observations are typically range and viewpoint dependent and that a map a discrete map representation might not be able to explain the full structure within a voxel. The localisation system uses a method to select submap based on how frequently and where from each submap was updated. This allow the system to select the most descriptive submap, thereby improving the localisation and increasing performance by up to 40%.

@inproceedings{adolfsson-2018-accuracy,
author = {Daniel Adolfsson and Stephanie Lowry and Henrik Andreasson},
title = {Improving Localisation Accuracy Using Submaps in Warehouses},
booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Workshop on Robotics for Logistics in Warehouses and Environments Shared with Humans},
year = {2018}
}