# Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft

arXiv: https://arxiv.org/abs/2111.06271 · HTML: https://mlsanity.com/paper/2111.06271/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs](https://mlsanity.com/paper/2205.03522/) (2022)
- [Visual Environment Assessment for Safe Autonomous Quadrotor Landing](https://mlsanity.com/paper/2311.10065/) (2023)
- [Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment](https://mlsanity.com/paper/2510.22204/) (2025)
- [Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing](https://mlsanity.com/paper/2409.09309/) (2024)
- [From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing](https://mlsanity.com/paper/2604.18918/) (2026)

## Abstract (arXiv)

In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Motion approach into a local, robot-centric, multi-resolution elevation map of the overflown terrain, which fuses depth measurements according to their lateral surface resolution (pixel-footprint) in a probabilistic framework based on the concept of dynamic Level of Detail. Map aggregation only requires depth maps and the associated poses, which are obtained from an onboard Visual Odometry algorithm. An efficient landing site detection method then exploits the features of the underlying multi-resolution map to detect safe landing sites based on slope, roughness, and quality of the reconstructed terrain surface. The evaluation of the performance of the mapping and landing site detection modules are analyzed independently and jointly in simulated and real-world experiments in order to establish the efficacy of the proposed approach.