The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Ford Campus vision and lidar data set
Open accessAbstract
In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November–December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A flexible new technique for camera calibration
Zhang, Z. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888718 · Cited by 15,004
A stochastic map for uncertain spatial relationships
Smith, R. K., Self, M. W., & Cheeseman, P. C. (1988) · Cited by 425
LCM: Lightweight Communications and Marshalling
Huang, A. S., Olson, E., & Moore, D. C. (2010) · 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2010.5649358 · Cited by 299
Extrinsic Calibration of a 3D Laser Scanner and an Omnidirectional Camera
Pandey, G., McBride, J., Savarese, S., & Eustice, R. M. (2010) · IFAC Proceedings Volumes · doi:10.3182/20100906-3-it-2019.00059 · Cited by 145
Self-citationProceedings of the 4th international symposium on Robotics Research
Bolles, R. C., & Roth, B. (1988) · Cited by 135
A passive solution to the sensor synchronization problem
Olson, E. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650579 · Cited by 65
Simultaneous local and global state estimation for robotic navigation
Moore, D. C., Huang, A. S., Walter, M. R., Olson, E., Fletcher, L., Leonard, J., & Teller, S. J. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152763 · Cited by 40
McBride, J. R., Ivan, J. C., Rhode, D., Rupp, J. D., Rupp, M. Y., Higgins, J. D., Turner, D. D., & Eustice, R. M. (2008) · Journal of Field Robotics · doi:10.1002/rob.20264 · Cited by 22
Self-citation
Cited by
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
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 3,048
Bags of Binary Words for Fast Place Recognition in Image Sequences
Galvez-López, D., & Tardos, J. D. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2197158 · Cited by 1,913
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632
Metadata
- Authors
- Pandey, G., McBride, J. R., & Eustice, R. M.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2011-03-11
- Volume
- 30
- Issue
- 13
- Pages
- 1543-1552
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W1995963656
- Cited by
- 360
- References
- 8
- FWCI
- 26.92
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Ford Campus vision and lidar data set. https://app.cochat.ai/papers/10.1177/0278364911400640
- The paper
- Pandey, G., McBride, J. R., & Eustice, R. M. (2011). Ford Campus vision and lidar data set. The International Journal of Robotics Research. https://doi.org/10.1177/0278364911400640
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
