# Deep Monocular Hazard Detection for Safe Small Body Landing

Source: [Deep Monocular Hazard Detection for Safe Small Body Landing](<https://www.sota2.com/research/paper/68fa6e721e209131cea3fd60>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:20:27.891Z

Authors: Travis Driver, Kento Tomita, Koki Ho, Panagiotis Tsiotras

Published: 2023-01-30T19:40:46.000Z

## Abstract

Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.13254>)

[PDF](<https://arxiv.org/pdf/2301.13254.pdf>)