A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions
When mobile robots are working in indoor unknown environments, the surrounding scenes are mainly low texture or repeating texture. This means that image features are easily lost when tracking the robots, and poses are difficult to estimate as the robot moves back and forth in a narrow area. In order...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8937551/ |
id |
doaj-7fc873b0ae024f9a8c3bd4344ab614b4 |
---|---|
record_format |
Article |
spelling |
doaj-7fc873b0ae024f9a8c3bd4344ab614b42021-03-29T23:16:09ZengIEEEIEEE Access2169-35362019-01-01718540818542110.1109/ACCESS.2019.29612668937551A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without OcclusionsChang Chen0https://orcid.org/0000-0001-7124-7869Hua Zhu1https://orcid.org/0000-0002-0737-1715Lei Wang2https://orcid.org/0000-0003-1828-4462Yu Liu3https://orcid.org/0000-0002-7268-5470School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, ChinaWhen mobile robots are working in indoor unknown environments, the surrounding scenes are mainly low texture or repeating texture. This means that image features are easily lost when tracking the robots, and poses are difficult to estimate as the robot moves back and forth in a narrow area. In order to improve such tracking problems, we propose a one-circle feature-matching method, which refers to a sequence of the circle matching for the time after space (STCM), and an STCM-based visual-inertial simultaneous localization and mapping (STCM-SLAM) technique. This strategy tightly couples the stereo camera and the inertial measurement unit (IMU) in order to better estimate poses of the mobile robot when working indoors. Forward backward optical flow is used to track image features. The absolute accuracy and relative accuracy of STCM increase by 37.869% and 129.167%, respectively, when compared with correlation flow. In addition, we compare our proposed method with other state-of-the-art methods. In terms of relative pose error, the accuracy of STCM-SLAM is an order of magnitude greater than ORB-SLAM2, and two orders of magnitude greater than OKVIS. Our experiments show that STCM-SLAM has obvious advantages over the OKVIS method, specifically in terms of scale error, running frequency, and CPU load. STCM-SLAM also performs the best under real-time conditions. In the indoor experiments, STCM-SLAM is able to accurately estimate the trajectory of the mobile robot. Based on the root mean square error, mean error, and standard deviation, the accuracy of STCM-SLAM is ultimately superior to that of either ORB-SLAM2 or OKVIS.https://ieeexplore.ieee.org/document/8937551/Indoor mobile robotsmulti-sensor fusionnonlinear optimizationSLAM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chang Chen Hua Zhu Lei Wang Yu Liu |
spellingShingle |
Chang Chen Hua Zhu Lei Wang Yu Liu A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions IEEE Access Indoor mobile robots multi-sensor fusion nonlinear optimization SLAM |
author_facet |
Chang Chen Hua Zhu Lei Wang Yu Liu |
author_sort |
Chang Chen |
title |
A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions |
title_short |
A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions |
title_full |
A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions |
title_fullStr |
A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions |
title_full_unstemmed |
A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions |
title_sort |
stereo visual-inertial slam approach for indoor mobile robots in unknown environments without occlusions |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
When mobile robots are working in indoor unknown environments, the surrounding scenes are mainly low texture or repeating texture. This means that image features are easily lost when tracking the robots, and poses are difficult to estimate as the robot moves back and forth in a narrow area. In order to improve such tracking problems, we propose a one-circle feature-matching method, which refers to a sequence of the circle matching for the time after space (STCM), and an STCM-based visual-inertial simultaneous localization and mapping (STCM-SLAM) technique. This strategy tightly couples the stereo camera and the inertial measurement unit (IMU) in order to better estimate poses of the mobile robot when working indoors. Forward backward optical flow is used to track image features. The absolute accuracy and relative accuracy of STCM increase by 37.869% and 129.167%, respectively, when compared with correlation flow. In addition, we compare our proposed method with other state-of-the-art methods. In terms of relative pose error, the accuracy of STCM-SLAM is an order of magnitude greater than ORB-SLAM2, and two orders of magnitude greater than OKVIS. Our experiments show that STCM-SLAM has obvious advantages over the OKVIS method, specifically in terms of scale error, running frequency, and CPU load. STCM-SLAM also performs the best under real-time conditions. In the indoor experiments, STCM-SLAM is able to accurately estimate the trajectory of the mobile robot. Based on the root mean square error, mean error, and standard deviation, the accuracy of STCM-SLAM is ultimately superior to that of either ORB-SLAM2 or OKVIS. |
topic |
Indoor mobile robots multi-sensor fusion nonlinear optimization SLAM |
url |
https://ieeexplore.ieee.org/document/8937551/ |
work_keys_str_mv |
AT changchen astereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT huazhu astereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT leiwang astereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT yuliu astereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT changchen stereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT huazhu stereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT leiwang stereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions AT yuliu stereovisualinertialslamapproachforindoormobilerobotsinunknownenvironmentswithoutocclusions |
_version_ |
1724189887065751552 |