Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

TL;DR

This paper introduces a Bayesian deep learning segmentation method for hazard detection in planetary landing sites, improving safety and reliability by quantifying uncertainty in terrain classification.

Contribution

It applies Bayesian deep learning to hazard detection, providing both safety predictions and uncertainty estimates to enhance autonomous landing safety.

Findings

The method effectively identifies safe landing sites with quantified uncertainty.

Performance degrades gracefully with increased sensor noise.

Uncertainty filtering improves safety prediction reliability.

Abstract

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs' reliability. In response to these limitations, this paper proposes an application of the Bayesian deep-learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by: (i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation; and (ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.