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Computer Science > Robotics

arXiv:2205.03522 (cs)
[Submitted on 7 May 2022]

Title:Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs

Authors:Pedro F. Proença, Jeff Delaune, Roland Brockers
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Abstract:The next generation of Mars rotorcrafts requires on-board autonomous hazard avoidance landing. To this end, this work proposes a system that performs continuous multi-resolution height map reconstruction and safe landing spot detection. Structure-from-Motion measurements are aggregated in a pyramid structure using a novel Optimal Mixture of Gaussians formulation that provides a comprehensive uncertainty model. Our multiresolution pyramid is built more efficiently and accurately than past work by decoupling pyramid filling from the measurement updates of different resolutions. To detect the safest landing location, after an optimized hazard segmentation, we use a mean shift algorithm on multiple distance transform peaks to account for terrain roughness and uncertainty. The benefits of our contributions are evaluated on real and synthetic flight data.
Comments: Accepted to ICRA 2022
Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2205.03522 [cs.RO]
  (or arXiv:2205.03522v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2205.03522

Submission history

From: Pedro F. Proença [view email]
[v1] Sat, 7 May 2022 02:16:29 UTC (10,659 KB)
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