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. and Kaess.
“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]
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.
“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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Localization in Inconsistent WiFi Environments
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- 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 › Engineering › Aerospace Engineering › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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