This paper presents an in-depth analysis of state-of-the-art semantic segmentation
algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance …

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 …

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 …

Semantic segmentation plays a significant role in unstructured and planetary scene
understanding, offering to a robotic system or a planetary rover valuable knowledge about …

In many scenarios it is desirable for planetary landers to select or modify their landing sites
autonomously during descent. We present a landing site selection algorithm which is …

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current
state-of-the-art methods leverage traditional computer vision approaches to automate …

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current
state-of-the-art methods leverage traditional computer vision approaches to automate …

In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying
only on images as observations. To process these images, convolutional neural networks …

View Video Presentation: https://doi. org/10.2514/6.2022-2207. vid Learning-based
autonomous hazard detection (HD) algorithms for planetary landing have been actively …

Abstract NASA's Human Landing System (HLS) is the next US space mission to land
astronauts on the moon. In this mission, the spacecraft will have to land safely on the moon …