Monocular image space tracking on a computationally limited MAV
We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operat...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/107201 https://orcid.org/0000-0001-9840-0552 https://orcid.org/0000-0002-8293-0492 |
| _version_ | 1826198005709012992 |
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| author | Ok, Kyel Gamage, Dinesh Drummond, Tom Dellaert, Frank Roy, Nicholas |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ok, Kyel Gamage, Dinesh Drummond, Tom Dellaert, Frank Roy, Nicholas |
| author_sort | Ok, Kyel |
| collection | MIT |
| description | We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations. The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy. |
| first_indexed | 2024-09-23T10:57:23Z |
| format | Article |
| id | mit-1721.1/107201 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:57:23Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1072012022-09-27T16:09:47Z Monocular image space tracking on a computationally limited MAV Ok, Kyel Gamage, Dinesh Drummond, Tom Dellaert, Frank Roy, Nicholas Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ok, Kyel Roy, Nicholas We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations. The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy. United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-0641) Micro Autonomous Consortium Systems and Technology 2017-03-06T21:39:24Z 2017-03-06T21:39:24Z 2015-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-6923-4 http://hdl.handle.net/1721.1/107201 Ok, Kyel et al. “Monocular Image Space Tracking on a Computationally Limited MAV.” IEEE, 2015. 6415–6422. https://orcid.org/0000-0001-9840-0552 https://orcid.org/0000-0002-8293-0492 en_US http://dx.doi.org/10.1109/ICRA.2015.7140100 Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other univ. web domain |
| spellingShingle | Ok, Kyel Gamage, Dinesh Drummond, Tom Dellaert, Frank Roy, Nicholas Monocular image space tracking on a computationally limited MAV |
| title | Monocular image space tracking on a computationally limited MAV |
| title_full | Monocular image space tracking on a computationally limited MAV |
| title_fullStr | Monocular image space tracking on a computationally limited MAV |
| title_full_unstemmed | Monocular image space tracking on a computationally limited MAV |
| title_short | Monocular image space tracking on a computationally limited MAV |
| title_sort | monocular image space tracking on a computationally limited mav |
| url | http://hdl.handle.net/1721.1/107201 https://orcid.org/0000-0001-9840-0552 https://orcid.org/0000-0002-8293-0492 |
| work_keys_str_mv | AT okkyel monocularimagespacetrackingonacomputationallylimitedmav AT gamagedinesh monocularimagespacetrackingonacomputationallylimitedmav AT drummondtom monocularimagespacetrackingonacomputationallylimitedmav AT dellaertfrank monocularimagespacetrackingonacomputationallylimitedmav AT roynicholas monocularimagespacetrackingonacomputationallylimitedmav |