# Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue

arXiv: https://arxiv.org/abs/1906.01304 · HTML: https://mlsanity.com/paper/1906.01304/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM](https://mlsanity.com/paper/2103.05056/) (2021)
- [3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention](https://mlsanity.com/paper/2203.10926/) (2022)
- [Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery](https://mlsanity.com/paper/2301.04581/) (2023)
- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [Certifying Emergency Landing for Safe Urban UAV](https://mlsanity.com/paper/2104.14928/) (2021)
- [iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images](https://mlsanity.com/paper/2209.06376/) (2022)
- [Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking](https://mlsanity.com/paper/2409.18533/) (2024)
- [Doing Right by Not Doing Wrong in Human-Robot Collaboration](https://mlsanity.com/paper/2202.02654/) (2022)
- [Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments](https://mlsanity.com/paper/2203.13792/) (2022)
- [Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment](https://mlsanity.com/paper/2510.22204/) (2025)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously navigate in disaster struck environments and land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as pre-existing maps cannot be leveraged for navigation due to structural changes that may have occurred. Furthermore, existing landing site detection algorithms are not suitable to identify safe landing regions on debris piles. In this work, we present a computationally efficient system for autonomous UAV navigation and landing that does not require any prior knowledge about the environment. We propose a novel landing site detection algorithm that computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy, and energy consumption information. We also introduce a first-of-a-kind synthetic dataset of over 1.2 million images of collapsed buildings with groundtruth depth, surface normals, semantics and camera pose information. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.