Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching
Self-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2017/1646095 |
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| _version_ | 1850169530226049024 |
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| author | Tianyang Cao Haoyuan Cai Dongming Fang Hui Huang Chang Liu |
| author_facet | Tianyang Cao Haoyuan Cai Dongming Fang Hui Huang Chang Liu |
| author_sort | Tianyang Cao |
| collection | DOAJ |
| description | Self-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects. Therefore, keyframes global map establishing method for robot localization in multiple rooms and corridors is needed. Content-based image matching is the core of this method. It is designed for the situation, by establishing keyframes containing both floor and distorted wall images. Image distortion, caused by robot view angle and movement, is analyzed and deduced. And an image matching solution is presented, consisting of extraction of overlap regions of keyframes extraction and overlap region rebuild through subblocks matching. For improving accuracy, ceiling points detecting and mismatching subblocks checking methods are incorporated. This matching method can process environment video effectively. In experiments, less than 5% frames are extracted as keyframes to build global map, which have large space distance and overlap each other. Through this method, robot can localize itself by matching its real-time vision frames with our keyframes map. Even with many similar objects/background in the environment or kidnapping robot, robot localization is achieved with position RMSE <0.5 m. |
| format | Article |
| id | doaj-art-a19e5dd513934b6a80de07d2d68286f2 |
| institution | OA Journals |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-a19e5dd513934b6a80de07d2d68286f22025-08-20T02:20:42ZengWileyJournal of Robotics1687-96001687-96192017-01-01201710.1155/2017/16460951646095Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image MatchingTianyang Cao0Haoyuan Cai1Dongming Fang2Hui Huang3Chang Liu4State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, ChinaSelf-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects. Therefore, keyframes global map establishing method for robot localization in multiple rooms and corridors is needed. Content-based image matching is the core of this method. It is designed for the situation, by establishing keyframes containing both floor and distorted wall images. Image distortion, caused by robot view angle and movement, is analyzed and deduced. And an image matching solution is presented, consisting of extraction of overlap regions of keyframes extraction and overlap region rebuild through subblocks matching. For improving accuracy, ceiling points detecting and mismatching subblocks checking methods are incorporated. This matching method can process environment video effectively. In experiments, less than 5% frames are extracted as keyframes to build global map, which have large space distance and overlap each other. Through this method, robot can localize itself by matching its real-time vision frames with our keyframes map. Even with many similar objects/background in the environment or kidnapping robot, robot localization is achieved with position RMSE <0.5 m.http://dx.doi.org/10.1155/2017/1646095 |
| spellingShingle | Tianyang Cao Haoyuan Cai Dongming Fang Hui Huang Chang Liu Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching Journal of Robotics |
| title | Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching |
| title_full | Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching |
| title_fullStr | Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching |
| title_full_unstemmed | Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching |
| title_short | Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching |
| title_sort | keyframes global map establishing method for robot localization through content based image matching |
| url | http://dx.doi.org/10.1155/2017/1646095 |
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