The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Event-Based Visual Inertial Odometry
Abstract
Event-based cameras provide a new visual sensing model by detecting changes in image intensity asynchronously across all pixels on the camera. By providing these events at extremely high rates (up to 1MHz), they allow for sensing in both high speed and high dynamic range situations where traditional cameras may fail. In this paper, we present the first algorithm to fuse a purely event-based tracking algorithm with an inertial measurement unit, to provide accurate metric tracking of a cameras full 6dof pose. Our algorithm is asynchronous, and provides measurement updates at a rate proportional to the camera velocity. The algorithm selects features in the image plane, and tracks spatiotemporal windows around these features within the event stream. An Extended Kalman Filter with a structureless measurement model then fuses the feature tracks with the output of the IMU. The camera poses from the filter are then used to initialize the next step of the tracker and reject failed tracks. We show that our method successfully tracks camera motion on the Event-Camera Dataset in a number of challenging situations.
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Cites
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“The actual corner detection is performed with FAST corners [17], with the image split into cells of fixed size, and the corner with the highest Shi-Tomasi score [18] within each cell being selected, as in [5].”
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationThe Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Zhu, A. Z., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800793 · Cited by 567
Self-citationEvents-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00398 · Cited by 420
Event-Based Moving Object Detection and Tracking
Mitrokhin, A., Fermuller, C., Parameshwara, C., & Aloimonos, Y. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593805 · Cited by 365
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00407 · Cited by 341
Metadata
- Authors
- Zhu, A. Z., Atanasov, N., & Daniilidis, K.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5816-5824
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2754360834
- Cited by
- 249
- References
- 2
- FWCI
- 39.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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