The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Real Time Localization and 3D Reconstruction
Abstract
In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.
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Cites
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ORB-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
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Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
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Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications
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FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
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Visual SLAM and Structure from Motion in Dynamic Environments: A Survey
Saputra, M. R. U., Markham, A., & Trigoni, N. (2019) · ACM Computing Surveys · doi:10.1145/3177853 · Cited by 398
Double window optimisation for constant time visual SLAM
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Sliding window filter with application to planetary landing
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“Representative examples include the distance and angle between observations in space [86], [99], [271], [342], specific time intervals [272], [343], and a minimum number of tracked landmarks [130], [141], [344]–[346].”
BackgroundAdaptive relative bundle adjustment
Sibley, G., Mei, C., Reid, I., & Newman, P. (2009) · Robotics: Science and Systems V · doi:10.15607/rss.2009.v.023 · Cited by 139
Edge Assisted Mobile Semantic Visual SLAM
Xu, J., Cao, H., Li, D., Huang, K., Qian, C., Shangguan, L., & Yang, Z. (2020) · IEEE INFOCOM 2020 - IEEE Conference on Computer Communications · doi:10.1109/infocom41043.2020.9155438 · Cited by 101
Vehicle localization using mono-camera and geo-referenced traffic signs
Qu, X., Soheilian, B., & Paparoditis, N. (2015) · 2015 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2015.7225751 · Cited by 85
Metadata
- Authors
- Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., & Sayd, P.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 363-370
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2124321582
- Cited by
- 396
- References
- 23
- FWCI
- 26.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
