Depth image based terrain recognition for supervisory control of a hybrid quadruped

Artur Saudabayev, Farabi Kungozhin, Damir Nurseitov, Huseyin Atakan Varol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents the depth image based locomotion strategy selection framework for a hybrid mobile robot. Terrain recognizer is a major component of a supervisory controller which classifies depth images into terrain types in real-time and selects different locomotion mode sub-controllers. In order to design the terrain recognizer, a database consisting of five terrain types (uneven, level ground, stair up, stair down and not traversable) is generated. Confidence based filtering is applied to enhance depth image data. The accuracy of the terrain classification for the testing database in five class terrain recognition problem is 96.71%. Real-world experiments conducted in mixed terrain environment evaluate both locomotion and terrain recognition capabilities of the robot in real-time. Experimental results show that a consumer depth camera might serve as an effective instrument for terrain recognition and thus locomotion strategy selection for hybrid robots with multiple locomotion modes.

Original languageEnglish
Title of host publicationIEEE International Symposium on Industrial Electronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1532-1537
Number of pages6
ISBN (Print)9781479923991
DOIs
Publication statusPublished - 2014
Event2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014 - Istanbul, Turkey
Duration: Jun 1 2014Jun 4 2014

Other

Other2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014
CountryTurkey
CityIstanbul
Period6/1/146/4/14

Fingerprint

Stairs
Robots
Controllers
Mobile robots
Cameras
Testing
Experiments

Keywords

  • depth image filtering
  • quadruped robot
  • RGB-Depth camera
  • supervisory control
  • terrain recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Saudabayev, A., Kungozhin, F., Nurseitov, D., & Varol, H. A. (2014). Depth image based terrain recognition for supervisory control of a hybrid quadruped. In IEEE International Symposium on Industrial Electronics (pp. 1532-1537). [6864842] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2014.6864842

Depth image based terrain recognition for supervisory control of a hybrid quadruped. / Saudabayev, Artur; Kungozhin, Farabi; Nurseitov, Damir; Varol, Huseyin Atakan.

IEEE International Symposium on Industrial Electronics. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1532-1537 6864842.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Saudabayev, A, Kungozhin, F, Nurseitov, D & Varol, HA 2014, Depth image based terrain recognition for supervisory control of a hybrid quadruped. in IEEE International Symposium on Industrial Electronics., 6864842, Institute of Electrical and Electronics Engineers Inc., pp. 1532-1537, 2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014, Istanbul, Turkey, 6/1/14. https://doi.org/10.1109/ISIE.2014.6864842
Saudabayev A, Kungozhin F, Nurseitov D, Varol HA. Depth image based terrain recognition for supervisory control of a hybrid quadruped. In IEEE International Symposium on Industrial Electronics. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1532-1537. 6864842 https://doi.org/10.1109/ISIE.2014.6864842
Saudabayev, Artur ; Kungozhin, Farabi ; Nurseitov, Damir ; Varol, Huseyin Atakan. / Depth image based terrain recognition for supervisory control of a hybrid quadruped. IEEE International Symposium on Industrial Electronics. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1532-1537
@inproceedings{f89317f0a169411b9e9fad9a2a377ef6,
title = "Depth image based terrain recognition for supervisory control of a hybrid quadruped",
abstract = "This paper presents the depth image based locomotion strategy selection framework for a hybrid mobile robot. Terrain recognizer is a major component of a supervisory controller which classifies depth images into terrain types in real-time and selects different locomotion mode sub-controllers. In order to design the terrain recognizer, a database consisting of five terrain types (uneven, level ground, stair up, stair down and not traversable) is generated. Confidence based filtering is applied to enhance depth image data. The accuracy of the terrain classification for the testing database in five class terrain recognition problem is 96.71{\%}. Real-world experiments conducted in mixed terrain environment evaluate both locomotion and terrain recognition capabilities of the robot in real-time. Experimental results show that a consumer depth camera might serve as an effective instrument for terrain recognition and thus locomotion strategy selection for hybrid robots with multiple locomotion modes.",
keywords = "depth image filtering, quadruped robot, RGB-Depth camera, supervisory control, terrain recognition",
author = "Artur Saudabayev and Farabi Kungozhin and Damir Nurseitov and Varol, {Huseyin Atakan}",
year = "2014",
doi = "10.1109/ISIE.2014.6864842",
language = "English",
isbn = "9781479923991",
pages = "1532--1537",
booktitle = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Depth image based terrain recognition for supervisory control of a hybrid quadruped

AU - Saudabayev, Artur

AU - Kungozhin, Farabi

AU - Nurseitov, Damir

AU - Varol, Huseyin Atakan

PY - 2014

Y1 - 2014

N2 - This paper presents the depth image based locomotion strategy selection framework for a hybrid mobile robot. Terrain recognizer is a major component of a supervisory controller which classifies depth images into terrain types in real-time and selects different locomotion mode sub-controllers. In order to design the terrain recognizer, a database consisting of five terrain types (uneven, level ground, stair up, stair down and not traversable) is generated. Confidence based filtering is applied to enhance depth image data. The accuracy of the terrain classification for the testing database in five class terrain recognition problem is 96.71%. Real-world experiments conducted in mixed terrain environment evaluate both locomotion and terrain recognition capabilities of the robot in real-time. Experimental results show that a consumer depth camera might serve as an effective instrument for terrain recognition and thus locomotion strategy selection for hybrid robots with multiple locomotion modes.

AB - This paper presents the depth image based locomotion strategy selection framework for a hybrid mobile robot. Terrain recognizer is a major component of a supervisory controller which classifies depth images into terrain types in real-time and selects different locomotion mode sub-controllers. In order to design the terrain recognizer, a database consisting of five terrain types (uneven, level ground, stair up, stair down and not traversable) is generated. Confidence based filtering is applied to enhance depth image data. The accuracy of the terrain classification for the testing database in five class terrain recognition problem is 96.71%. Real-world experiments conducted in mixed terrain environment evaluate both locomotion and terrain recognition capabilities of the robot in real-time. Experimental results show that a consumer depth camera might serve as an effective instrument for terrain recognition and thus locomotion strategy selection for hybrid robots with multiple locomotion modes.

KW - depth image filtering

KW - quadruped robot

KW - RGB-Depth camera

KW - supervisory control

KW - terrain recognition

UR - http://www.scopus.com/inward/record.url?scp=84907379128&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907379128&partnerID=8YFLogxK

U2 - 10.1109/ISIE.2014.6864842

DO - 10.1109/ISIE.2014.6864842

M3 - Conference contribution

SN - 9781479923991

SP - 1532

EP - 1537

BT - IEEE International Symposium on Industrial Electronics

PB - Institute of Electrical and Electronics Engineers Inc.

ER -