Next Article in Journal
A Shoe-Embedded Piezoelectric Energy Harvester for Wearable Sensors
Previous Article in Journal
Bidirectional and Multi-User Telerehabilitation System: Clinical Effect on Balance, Functional Activity, and Satisfaction in Patients with Chronic Stroke Living in Long-Term Care Facilities
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(7), 12467-12496; doi:10.3390/s140712467

Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor

Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Korea
*
Author to whom correspondence should be addressed.
Received: 28 April 2014 / Revised: 25 June 2014 / Accepted: 27 June 2014 / Published: 11 July 2014
(This article belongs to the Section Physical Sensors)

Abstract

In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause groups of false loop closing when the same moved objects are observed for a while, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, to prune the falsely grouped constraints efficiently, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. Next, false constraints of the pose graph are pruned according to an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system. View Full-Text
Keywords: simultaneous localization and mapping (SLAM); low dynamic environment; pose graph; RGB-D (red-green-blue depth) simultaneous localization and mapping (SLAM); low dynamic environment; pose graph; RGB-D (red-green-blue depth)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lee, D.; Myung, H. Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor. Sensors 2014, 14, 12467-12496.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top