Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
This work addresses safety-critical hazard detection for autonomous space missions, offering a more reliable method compared to traditional approaches, though it is incremental in applying Bayesian deep learning to this domain.
The paper tackles the problem of hazard detection for autonomous planetary landing by proposing a Bayesian deep-learning segmentation method that generates safety prediction and uncertainty maps, filtering uncertain pixels to improve reliability; experiments on simulated Mars data show performance gains under varying noise levels.
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.