# HALO: Hazard-Aware Landing Optimization for Autonomous Systems

arXiv: https://arxiv.org/abs/2304.01583 · HTML: https://mlsanity.com/paper/2304.01583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing](https://mlsanity.com/paper/2102.10545/) (2021)
- [Convex Optimization for Trajectory Generation](https://mlsanity.com/paper/2106.09125/) (2021)
- [Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs](https://mlsanity.com/paper/2205.03522/) (2022)

## Built on by (most cited first, 1 of 1)

- [Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection](https://mlsanity.com/paper/2603.14639/) (2026)

## Abstract (arXiv)

With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO), to address the perception and planning challenges, respectively. The HALSS framework processes point cloud information to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target contingency planner that adaptively replans as new perception information is received. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success whilst being more fuel efficient compared to a nonadaptive DDTO approach.