The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM
Open accessAbstract
In this letter, we use two-dimensional (2-D) object detections from multiple views to simultaneously estimate a 3-D quadric surface for each object and localize the camera position. We derive a simultaneous localization and mapping (SLAM) formulation that uses dual quadrics as 3-D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2-D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Gradient-based learning applied to document recognition
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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SSD: Single Shot MultiBox Detector
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Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization
Hughes, N., Chang, Y., & Carlone, L. (2022) · Robotics: Science and Systems XVIII · doi:10.15607/rss.2022.xviii.050 · Cited by 202
Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation
Werby, A., Huang, C., Büchner, M., Valada, A., & Burgard, W. (2024) · Robotics: Science and Systems XX · doi:10.15607/rss.2024.xx.077 · Cited by 124
A review of research on slam technology based on the fusion of lidar and vision
Chen, P., Zhao, X., Zeng, L., Liu, L., Liu, S., Sun, L., Li, Z., Chen, H., Liu, G., Qiao, Z., et al. (2025) · Sensors · doi:10.3390/s25051447 · Cited by 46
Metadata
- Authors
- Nicholson, L., Milford, M., & Sunderhauf, N.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2019-01
- Volume
- 4
- Issue
- 1
- Pages
- 1-8
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2887751724
- Cited by
- 325
- References
- 37
- FWCI
- 18.21
- Type
- journal article
- Language
- en
- Open access
- green
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- Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2018.2866205
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