A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor
Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/8/19937 |
| _version_ | 1828118145358888960 |
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| author | Liang Zhang Peiyi Shen Guangming Zhu Wei Wei Houbing Song |
| author_facet | Liang Zhang Peiyi Shen Guangming Zhu Wei Wei Houbing Song |
| author_sort | Liang Zhang |
| collection | DOAJ |
| description | Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy. |
| first_indexed | 2024-04-11T13:27:36Z |
| format | Article |
| id | doaj.art-3b23ab6a8eea4abab49f5d57d43981d6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:27:36Z |
| publishDate | 2015-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3b23ab6a8eea4abab49f5d57d43981d62022-12-22T04:22:00ZengMDPI AGSensors1424-82202015-08-01158199371996710.3390/s150819937s150819937A Fast Robot Identification and Mapping Algorithm Based on Kinect SensorLiang Zhang0Peiyi Shen1Guangming Zhu2Wei Wei3Houbing Song4National School of Software, Xidian University, Xi’an 710071, ChinaNational School of Software, Xidian University, Xi’an 710071, ChinaNational School of Software, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology,5 Jinhua S. Rd., Xi’an 710048, ChinaDepartment of Electrical and Computer Engineering, West Virginia University, 405 Fayette Pike, Montgomery, WV 25136, USAInternet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.http://www.mdpi.com/1424-8220/15/8/19937SLAMKinectRGB-D SLAMimproved algorithmDr Robot X80 |
| spellingShingle | Liang Zhang Peiyi Shen Guangming Zhu Wei Wei Houbing Song A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor Sensors SLAM Kinect RGB-D SLAM improved algorithm Dr Robot X80 |
| title | A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor |
| title_full | A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor |
| title_fullStr | A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor |
| title_full_unstemmed | A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor |
| title_short | A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor |
| title_sort | fast robot identification and mapping algorithm based on kinect sensor |
| topic | SLAM Kinect RGB-D SLAM improved algorithm Dr Robot X80 |
| url | http://www.mdpi.com/1424-8220/15/8/19937 |
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