Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance to obstacles from richer sensor readings such as 3D lasers or RGBD sensors, we present a method to estimate the distance directly from raw 2D laser data. To learn a mapping from raw 2D laser distances to obstacle distances we frame the problem as a learning task and train a neural network formed as an autoencoder. A novel configuration of network hyperparameters is proposed for the task at hand and is quantitatively validated on a test set. Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the trained network can successfully infer obstacle distances from partial 2D laser readings.
Original languageEnglish
Title of host publicationProceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Place of PublicationMadrid, Spain
PublisherIEEE
Pages4781-4787
Number of pages7
ISBN (Electronic)978-1-5386-8094-0
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Madrid Municipal Conference Centre (MMCC), Madrid, Spain
Duration: 1 Oct 20185 Oct 2018
https://www.iros2018.org/

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Country/TerritorySpain
CityMadrid
Period01/10/201805/10/2018
Internet address

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