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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Low-latency visual odometry using event-based feature tracks |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 1 - 8 |
| Event Title | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Event Type | conference |
| Event Location | Daejeon, Korea |
| Event Start Date | October 9 - 2016 |
| Event End Date | October 14 - 2016 |
| Place of Publication | Daejeon, Korea |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Abstract Text | New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional camera and an event-based sensor in the same pixel array. These sensors have great potential for robotics because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. In this paper, we present a lowlatency visual odometry algorithm for the DAVIS sensor using event-based feature tracks. Features are first detected in the grayscale frames and then tracked asynchronously using the stream of events. The features are then fed to an event-based visual odometry algorithm that tightly interleaves robust pose optimization and probabilistic mapping. We show that our method successfully tracks the 6-DOF motion of the sensor in natural scenes. This is the first work on event-based visual odometry with the DAVIS sensor using feature tracks. |
| Free access at | Official URL |
| Official URL | http://rpg.ifi.uzh.ch/docs/IROS16_Kueng.pdf |
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| Digital Object Identifier | 10.1109/IROS.2016.7758089 |
| Other Identification Number | merlin-id:13507 |
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