Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12414 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910647284924416 |
|---|---|
| author | Bhaskara, Ramchander Georgakis, Georgios Nash, Jeremy Cameron, Marissa Bowkett, Joseph Ansar, Adnan Majji, Manoranjan Backes, Paul |
| author_facet | Bhaskara, Ramchander Georgakis, Georgios Nash, Jeremy Cameron, Marissa Bowkett, Joseph Ansar, Adnan Majji, Manoranjan Backes, Paul |
| contents | Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12414 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy Bhaskara, Ramchander Georgakis, Georgios Nash, Jeremy Cameron, Marissa Bowkett, Joseph Ansar, Adnan Majji, Manoranjan Backes, Paul Computer Vision and Pattern Recognition Graphics Robotics Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss. |
| title | Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy |
| topic | Computer Vision and Pattern Recognition Graphics Robotics |
| url | https://arxiv.org/abs/2401.12414 |