Cited by 203Advanced Neural Network ApplicationsRobotics and Sensor-Based LocalizationRemote Sensing and LiDAR Applications

3D Convolutional Neural Networks for landing zone detection from LiDAR

Maturana, Daniel, Scherer, Sebastian·2015·2015 IEEE International Conference on Robotics and Automation (ICRA)

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.

Integrity signals

Registry

Crossref · 10.1109/icra.2015.7139679

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection, path planning, and TRN even in the case of uncooperative operations.

    “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.”

Cites

Flagged first, then by citations

4 of the 38 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Maturana, D., & Scherer, S.
Affiliations
Carnegie Mellon University (US)
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 › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
  • Physical Sciences › Environmental Science › Environmental Engineering › 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