The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Inverse Depth Parametrization for Monocular SLAM
Abstract
We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in'' smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a EuclideanXYZrepresentation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the EuclideanXYZform, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and also real time 360degloop closing.
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Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
MonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Self-citation
Cited by
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898
“, points whose depth cannot be reliably estimated due to little disparity in the stereo camera), using an inverse depth parameterization [6] for the latter.”
MethodORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Self-citationMonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Self-citationHigh-precision, consistent EKF-based visual-inertial odometry
Li, M., & Mourikis, A. I. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913481251 · Cited by 813
Strasdat, H., Montiel, J. M. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.009 · Cited by 439
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Metadata
- Authors
- Civera, J., Davison, A. J., & Montiel, J.
- Venue
- IEEE Transactions on Robotics
- Published
- 2008-10
- Volume
- 24
- Issue
- 5
- Pages
- 932-945
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2118428504
- Cited by
- 779
- References
- 2
- FWCI
- 88.18
- Type
- journal article
- Language
- en
- Open access
- closed
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