Giovanni Molina

h-index4
2papers
223citations

2 Papers

4.8CVApr 1, 2022
Hazard Detection And Avoidance For The Nova-C Lander

Joel Getchius, Devin Renshaw, Daniel Posada et al.

In early 2022, Intuitive Machines' NOVA-C Lander will touch down on the lunar surface becoming the first commercial endeavor to visit a celestial body. NOVA-C will deliver six payloads to the lunar surface with various scientific and engineering objectives, ushering in a new era of commercial space exploration and utilization. However, to safely accomplish the mission, the NOVA-C lander must ensure its landing site is free of hazards larger than 30 cm and the slope of local terrain at touchdown is less than 10 degrees off vertical. To accomplish this, NOVA-C utilizes Intuitive Machines' precision navigation system, coupled with machine vision algorithms for scene reduction and landing site characterization. A unique aspect to the NOVA-C approach is the real-time nature of the hazard detection and avoidance algorithms--which are performed 400 meters above and down range of the intended landing site and completed within 15 seconds. In this paper, we review the theoretical foundations for the hazard detection and avoidance algorithms, describe the practical challenges of implementation on the NOVA-C flight computer, and present test and analysis results.

31.4CLSep 28, 2019
Overview for the Second Shared Task on Language Identification in Code-Switched Data

Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim et al.

We present an overview of the second shared task on language identification in code-switched data. For the shared task, we had code-switched data from two different language pairs: Modern Standard Arabic-Dialectal Arabic (MSA-DA) and Spanish-English (SPA-ENG). We had a total of nine participating teams, with all teams submitting a system for SPA-ENG and four submitting for MSA-DA. Through evaluation, we found that once again language identification is more difficult for the language pair that is more closely related. We also found that this year's systems performed better overall than the systems from the previous shared task indicating overall progress in the state of the art for this task.