Monocular SLAM for Autonomous Robots with Enhanced Features Initialization
This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are intr...
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doaj-41474279f0c84bf58344ff2fc785efc12020-11-25T00:47:01ZengMDPI AGSensors1424-82202014-04-011446317633710.3390/s140406317s140406317Monocular SLAM for Autonomous Robots with Enhanced Features InitializationEdmundo Guerra0Rodrigo Munguia1Antoni Grau2Automatic Control Department, Technical University of Catalonia UPC, 08028 Barcelona, SpainComputer Science Department, CUCEI, Universidad de Guadalajara, 44430 Guadalajara, JAL, MexicoAutomatic Control Department, Technical University of Catalonia UPC, 08028 Barcelona, SpainThis work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DI-D monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided.http://www.mdpi.com/1424-8220/14/4/6317monocular SLAMhuman-robot interactionHRIstereo matchingdepth estimation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Edmundo Guerra Rodrigo Munguia Antoni Grau |
spellingShingle |
Edmundo Guerra Rodrigo Munguia Antoni Grau Monocular SLAM for Autonomous Robots with Enhanced Features Initialization Sensors monocular SLAM human-robot interaction HRI stereo matching depth estimation |
author_facet |
Edmundo Guerra Rodrigo Munguia Antoni Grau |
author_sort |
Edmundo Guerra |
title |
Monocular SLAM for Autonomous Robots with Enhanced Features Initialization |
title_short |
Monocular SLAM for Autonomous Robots with Enhanced Features Initialization |
title_full |
Monocular SLAM for Autonomous Robots with Enhanced Features Initialization |
title_fullStr |
Monocular SLAM for Autonomous Robots with Enhanced Features Initialization |
title_full_unstemmed |
Monocular SLAM for Autonomous Robots with Enhanced Features Initialization |
title_sort |
monocular slam for autonomous robots with enhanced features initialization |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2014-04-01 |
description |
This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DI-D monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided. |
topic |
monocular SLAM human-robot interaction HRI stereo matching depth estimation |
url |
http://www.mdpi.com/1424-8220/14/4/6317 |
work_keys_str_mv |
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