The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
Visual Navigation Using Heterogeneous Landmarks and Unsupervised Geometric Constraints
Abstract
We present a heterogeneous landmark-based visual navigation approach for a monocular mobile robot. We utilize heterogeneous visual features, such as points, line segments, lines, planes, and vanishing points, and their inner geometric constraints managed by a novel multilayer feature graph (MFG). Our method extends the local bundle adjustment-based visual simultaneous localization and mapping (SLAM) framework by explicitly exploiting the heterogeneous features and their inner geometric relationships in an unsupervised manner. As the result, our heterogeneous landmark-based visual navigation algorithm takes a video stream as input, initializes and iteratively updates MFG based on extracted key frames, and refines robot localization and MFG landmarks through the process. We present pseudocode for the algorithm and analyze its complexity. We have evaluated our method and compared it with state-of-the-art point landmark-based visual SLAM methods using multiple indoor and outdoor datasets. In particular, on the KITTI dataset, our method reduces the translational error by 52.5% under urban sequences where rectilinear structures dominate the scene.
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“Particularly simple boundary representations are plane-based models, which have been used for mapping by Castle et al. [44] and Kaess [124, 162].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [162]
In the citing paper
“While a large body of work focuses on the estimation of point features, the robotics literature includes extensions to more complex geometric landmarks, including lines, segments, or arcs [162].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [162]
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
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
Thrun, S. (2002) · CACM · doi:10.1145/504729.504754 · Cited by 7,941
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 2×
“Lower level landmarks such as corners [30] and SIFT points [31] are relatively easy to use due to their geometric simplicity.”
BackgroundMethodParallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
EPnP: An Accurate O(n) Solution to the PnP Problem
Lepetit, V., Moreno-Noguer, F., & Fua, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0152-6 · Cited by 3,298
Multiple view geometry in computer vision
Opower, H. (2002) · Optics and Lasers in Engineering · doi:10.1016/s0143-8166(01)00145-2 · Cited by 3,044
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
G2o: A general framework for graph optimization
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., & Burgard, W. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979949 · Cited by 2,055
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094
González-García, J., Gómez-Espinosa, A., Cuan‐Urquizo, E., García-Valdovinos, L. G., Salgado-Jiménez, T., & Cabello, J. A. E. (2020) · Applied Sciences · doi:10.3390/app10041256 · Cited by 287
Recent Trends in Underwater Visible Light Communication (UVLC) Systems
Ali, M. F., Jayakody, D. N. K., & Li, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3150093 · Cited by 277
AIPNet: Image-to-Image Single Image Dehazing With Atmospheric Illumination Prior
Wang, A., Wang, W., Liu, J., & Gu, N. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2868567 · Cited by 152
Metadata
- Authors
- Lu, Y., & Song, D.
- Affiliations
- Texas A&M University (US)
- ORCID
- Venue
- IEEE Transactions on Robotics
- Published
- 2015-06
- Volume
- 31
- Issue
- 3
- Pages
- 736-749
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W645436802
- Cited by
- 103
- References
- 64
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 17 · 2020: 9 · 2019: 13 · 2018: 12 · 2017: 8 · 2016: 15 · 2015: 3
- FWCI
- 18.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Funders
- National Science Foundation (IIS-1318638, NRI-1426752)
- TxDot (0-6869)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- visual navigation, visual SLAM, bundle adjustment, vanishing points, robot localization, key frame extraction
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