تقرير
Deep Monocular Hazard Detection for Safe Small Body Landing
| العنوان: | Deep Monocular Hazard Detection for Safe Small Body Landing |
|---|---|
| المؤلفون: | Driver, Travis, Tomita, Kento, Ho, Koki, Tsiotras, Panagiotis |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | 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. Comment: Presented at the AAS/AIAA Space Flight Mechanics Meeting, January 14-19, 2023, Austin, TX, USA |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2301.13254Test |
| رقم الانضمام: | edsarx.2301.13254 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |