Semantic Localization System for Robots at Large Indoor Environments Based on Environmental Stimuli.

Clicks: 262
ID: 105462
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #906 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In this paper, we present a new procedure to solve the global localization of mobile robots called Environmental Stimulus Localization (ESL). We propose that the presence of common facts on the environment around the robot can be considered as stimuli for the procedure. The robust performance of our approach is supported by two concurrent particle filters. A primary particle filter estimates and tracks the robot position, while a secondary filter is fired by environmental stimuli, helps to reduce the influence of measurement errors and allows an earlier recovery from localization failures. We have successfully used this method in a 5000 m 2 real indoor environment using as inputs the available environment information from a Geographical Information System (GIS) map, the robot's odometry and the output of an algorithm for the perception of facts from the environment. We present a case study and the result of different tests, showing the performance of our method under the influence of errors in real applications.
Reference Key
serrano2020semanticsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Serrano, Fco-Javier;Moreno, Vidal;Curto, Belén;Álves, Raul;
Journal sensors
Year 2020
DOI
E2116
URL
Keywords
indoor localization gis maps particle filters search and rescue tasks

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.