The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
SegMap: 3D Segment Mapping using Data-Driven Descriptors
Open accessAbstract
When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination.This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds.In addition to facilitating the computationally intensive task of processing 3D point clouds, working at the level of segments addresses the data compression requirements of real-time single-and multi-robot systems.While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.This is particularly interesting for navigation tasks and for providing visual feedback to endusers such as robot operators, for example in search and rescue scenarios.These capabilities are demonstrated in multiple urban driving and search and rescue experiments.Our method leads to an increase of area under the ROC curve of 28.3% over current state of the art using eigenvalue based features.We also obtain very similar reconstruction capabilities to a model specifically trained for this task.The SegMap implementation is available open-source along with easy to run demonstrations at www.github.com/ethz-asl/segmap.
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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
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., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Self-citationSIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393 · cited 1×
“For 3D point clouds methods based on CNNs achieve impressive performances in applications such as object detection [12, 19, 21, 25, 27, 34, 37], semantic segmentation [19, 25, 27, 30], and 3D object generation [36].”
MethodiSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362
Visual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577
Cited by
(2020) · doi:10.1109/icra40945.2020.9196885 · Cited by 633
The current state and future outlook of rescue robotics
Delmerico, J., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Horvat, T., Cadena, C., Hutter, M., Ijspeert, A., Floreano, D., et al. (2019) · Journal of Field Robotics · doi:10.1002/rob.21887 · Cited by 371
Self-citationAutonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
Cattaneo, D., Vaghi, M., & Valada, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3150683 · Cited by 258
Tinchev, G., Penate-Sanchez, A., & Fallon, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2895264 · Cited by 51
“We provide comparisons against a popular method for segment-based localization, SegMatch,1 as proposed in [5], a data-driven incremental approach SegMap1 [6], and our previous work NSM [7].”
BackgroundMethodInfluential
Metadata
- Authors
- Dubé, R., Cramariuc, A., Dugas, D., Nieto, J., Siegwart, R., & Cadena, C.
- Venue
- Robotics: Science and Systems XIV
- Published
- 2018-06-26
- OpenAlex
- W2798278946
- Cited by
- 186
- References
- 8
- FWCI
- 42.16
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Dubé, R., Cramariuc, A., Dugas, D., Nieto, J., Siegwart, R., & Cadena, C. (2018). SegMap: 3D Segment Mapping using Data-Driven Descriptors. Robotics: Science and Systems XIV. https://doi.org/10.15607/rss.2018.xiv.003
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