mlsanityData to 7 Oct 2026

Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs

arXiv 2205.03522 ↗First posted 7 May 2022cs.RO cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 26 references that are in the map. First posted on arXiv 7 May 2022.

Builds on

6 of 26 references.

References · 6

  1. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  2. 2017Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoringcited by 8
  3. 2020xVIO: A Range-Visual-Inertial Odometry Frameworkcited by 5
  4. 2021Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraftcited by 5
  5. 2017Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertaintycited by 3
  6. 2018Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.