The DOI matches its Crossref record; 34 of the 38 references it cites were found in a registry or an index.
3D Convolutional Neural Networks for landing zone detection from LiDAR
Abstract
We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficient and highly accurate system for detection tasks from raw occupancy data. We apply this method to the problem of detecting safe landing zones for autonomous helicopters from LiDAR point clouds. Current methods for this problem rely on heuristic rules and use simple geometric features. These heuristics break down in the presence of low vegetation, as they do not distinguish between vegetation that may be landed on and solid objects that should be avoided. We evaluate the system with a combination of real and synthetic range data. We show our system outperforms various benchmarks, including a system integrating various hand-crafted point cloud features from the literature.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection20, path planning21, and TRN even in the case of uncooperative operations22.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [20]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
(2014) · Cited by 33,419
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
(1989) · neural information processing systems · Cited by 3,736
High resolution maps from wide angle sonar
Moravec, H., & Elfes, A. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1985.1087316 · Cited by 1,772
Deep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914549607 · Cited by 1,683
Theano: A CPU and GPU Math Compiler in Python
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J. P., Warde-Farley, D., & Bengio, Y. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-003 · Cited by 1,063
A Fast Voxel Traversal Algorithm for Ray Tracing
Amanatides, J., & Woo, A. (1987) · doi:10.2312/egtp.19871000 · Cited by 669
Research works printed but not linked: 2
- cun, 1990, Handwritten digit recognition with a backpropagation network, NIPS, 396
- 2007, Helipad Marking And Lighting (STANAG 3619)
Cited by
Sorted by citations
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,752
Self-citationFoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00029 · Cited by 1,365
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.29 · Cited by 1,172
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967
Metadata
- Authors
- Maturana, D., & Scherer, S.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Scherer, Sebastian
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 3471-3478
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1493004075
- Cited by
- 203
- References
- 38
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 7 · 2023: 12 · 2022: 15 · 2021: 20 · 2020: 24 · 2019: 34 · 2018: 33 · 2017: 27 · 2016: 18 · 2015: 3
- FWCI
- 11.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
- Keywords
- LiDAR point cloud, 3D convolutional neural networks, obstacle detection, vegetated terrain, autonomous helicopter
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
