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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.
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“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]
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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 ›
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