oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation
Örebro University, School of Science and Technology. (MRO Lab)ORCID iD: 0000-0002-3079-0512
Örebro University, School of Science and Technology. (MRO Lab)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (MRO Lab)ORCID iD: 0000-0003-0217-9326
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.

Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values.

We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method.

We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 4993-4999
Keywords [en]
map segmentation, free space, layout
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-68421DOI: 10.1109/ICRA.2018.8461128ISI: 000446394503114OAI: oai:DiVA.org:oru-68421DiVA, id: diva2:1237531
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBotKnowledge Foundation, 20140220Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-10-10Bibliographically approved
In thesis
1. Helping robots help us: Using prior information for localization, navigation, and human-robot interaction
Open this publication in new window or tab >>Helping robots help us: Using prior information for localization, navigation, and human-robot interaction
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Maps are often used to provide information and guide people. Emergency maps or floor plans are often displayed on walls and sketch maps can easily be drawn to give directions. However, robots typically assume that no knowledge of the environment is available before exploration even though making use of prior maps could enhance robotic mapping. For example, prior maps can be used to provide map data of places that the robot has not yet seen, to correct errors in robot maps, as well as to transfer information between map representations.

I focus on two types of prior maps representing the walls of an indoor environment: layout maps and sketch maps. I study ways to relate information of sketch or layout maps with an equivalent metric map and study how to use layout maps to improve the robot’s mapping. Compared to metric maps such as sensor-built maps, layout and sketch maps can have local scale errors or miss elements of the environment, which makes matching and aligning such heterogeneous map types a hard problem.

I aim to answer three research questions: how to interpret prior maps by finding meaningful features? How to find correspondences between the features of a prior map and a metric map representing the same environment? How to integrate prior maps in SLAM so that both the prior map and the map built by the robot are improved?

The first contribution of this thesis is an algorithm that can find correspondences between regions of a hand-drawn sketch map and an equivalent metric map and achieves an overall accuracy that is within 10% of that of a human. The second contribution is a method that enables the integration of layout map data in SLAM and corrects errors both in the layout and the sensor map.

These results provide ways to use prior maps with local scale errors and different levels of detail, whether they are close to metric maps, e.g. layout maps, or non-metric maps, e.g. sketch maps. The methods presented in this work were used in field tests with professional fire-fighters for search and rescue applications in low-visibility environments. A novel radar sensor was used to perform SLAM in smoke and, using a layout map as a prior map, users could indicate points of interest to the robot on the layout map, not only during and after exploration, but even before it took place.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2019. p. 83
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 86
Keywords
graph-based SLAM, prior map, sketch map, emergency map, map matching, graph matching, segmentation, search and rescue
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-75877 (URN)978-91-7529-299-1 (ISBN)
Public defence
2019-10-29, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-10-02Bibliographically approved

Open Access in DiVA

A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation(1264 kB)107 downloads
File information
File name FULLTEXT01.pdfFile size 1264 kBChecksum SHA-512
cc9b923f155f98c569f9cfa1cd8b53e07cd90646eb59c45188bbe9de426dd58c8e1db53f2963bf1ed30002e806324a9da90a0b6ace30e265e66c507d8c410b39
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Mielle, MalcolmMagnusson, MartinLilienthal, Achim J.

Search in DiVA

By author/editor
Mielle, MalcolmMagnusson, MartinLilienthal, Achim J.
By organisation
School of Science and Technology
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 107 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 3046 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf