CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations
The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2020/7362952 |
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| _version_ | 1832550590396235776 |
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| author | Trung Nguyen George K. I. Mann Andrew Vardy Raymond G. Gosine |
| author_facet | Trung Nguyen George K. I. Mann Andrew Vardy Raymond G. Gosine |
| author_sort | Trung Nguyen |
| collection | DOAJ |
| description | The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution. |
| format | Article |
| id | doaj-art-c8c14e4cb691416199b4d072cf0a9465 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-c8c14e4cb691416199b4d072cf0a94652025-02-03T06:06:26ZengWileyJournal of Robotics1687-96001687-96192020-01-01202010.1155/2020/73629527362952CKF-Based Visual Inertial Odometry for Long-Term Trajectory OperationsTrung Nguyen0George K. I. Mann1Andrew Vardy2Raymond G. Gosine3Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, CanadaFaculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, CanadaDepartment of Computer Science and Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, CanadaFaculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, CanadaThe estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution.http://dx.doi.org/10.1155/2020/7362952 |
| spellingShingle | Trung Nguyen George K. I. Mann Andrew Vardy Raymond G. Gosine CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations Journal of Robotics |
| title | CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations |
| title_full | CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations |
| title_fullStr | CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations |
| title_full_unstemmed | CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations |
| title_short | CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations |
| title_sort | ckf based visual inertial odometry for long term trajectory operations |
| url | http://dx.doi.org/10.1155/2020/7362952 |
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