| Download | - View final version: AI-based landing zone detection for vertical takeoff and land LiDAR localization and mapping pipelines (PDF, 6.7 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1139/dsa-2022-0038 |
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| Author | Search for: Balasooriya, Narmada M.1ORCID identifier: https://orcid.org/0000-0002-0651-6572; Search for: De Silva, Oscar1; Search for: Jayasiri, Awantha2; Search for: Mann, George K.I.1 |
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| Affiliation | - Memorial University of Newfoundland
- National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Subject | vertical take-off and landing vehicles; landing zone detection; LiDAR; deep learning; neural networks; VLOAM |
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| Abstract | This work describes a deep learning-based autonomous landing zone identification module for a vertical takeoff and landing vehicle. The proposed module is developed using LiDAR point cloud data and can be integrated into a visual LiDAR odometry and mapping pipeline implemented in the vehicle. “ConvPoint,” the top-performing neural network architecture in an online point cloud segmentation benchmark leaderboard at the time of writing, was chosen as the reference architecture. Semantic labeling of the datasets was done using the terrain geometry characteristics and manual adjustment of labels through visual observation. Point clouds captured by the Memorial University and online point cloud datasets were used to transfer-learn the neural network model and to evaluate the accuracy-runtime trade-off for the proposed pipeline. The selected neural network model generated accuracy values of 89.7% and 92.1% on two selected datasets, while it computed 3940.15 points per second and 3633.85 points per second to predict landing zone labels, respectively. Hyperparameter tuning was carried out to obtain a higher throughput with an update rate of 1 Hz for the landing zone map of the point cloud inputs from the visual LiDAR odometry and mapping pipeline. The proposed system is validated by evaluating its performance on three variations of point clouds. The results validate the accuracy-runtime trade-off of the proposed system and show that further optimization can improve performance. |
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| Publication date | 2024-04-02 |
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| Publisher | Canadian Science Publishing |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c312c700-fe1b-4cc9-bbe1-7e2cb1b6db2c |
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| Record created | 2024-09-06 |
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| Record modified | 2024-09-06 |
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