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

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

email and * email
Received: 28 April 2014; in revised form: 25 June 2014 / Accepted: 27 June 2014 / Published: 11 July 2014
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: simultaneous localization and mapping (SLAM); low dynamic environment; pose graph; RGB-D (red-green-blue depth)
PDF Full-text Download PDF Full-Text [17700 KB, uploaded 11 July 2014 14:19 CEST]

Export to BibTeX |
EndNote


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.

AMA 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(7):12467-12496.

Chicago/Turabian Style

Lee, Donghwa; Myung, Hyun. 2014. "Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor." Sensors 14, no. 7: 12467-12496.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert