Research/ PLANETARY ROBOTICS
Azure Kinect à La Luna (AKALL): Leveraging Low-Cost RGB and Depth-Camera in Lunar Exploration
Don Derek Haddad, Cody A. Paige, Ferrous Ward, Joseph A. Paradiso, Dava J. Newman, Ariel Ekblaw, Amanda M. Cook, Jennifer L. Heldmann
Perception & State EstimationOpenAlex topic: Robotics and Sensor-Based Localization
CLPSnamed in abstract, keywords
The Azure Kinect à La Luna (AKALL) project applies the advanced abilities of a space graded and modified Microsoft Azure Kinect RGB and depth-camera to allow low-cost and low-bandwidth local 3D reconstruction. The AKALL application is encapsulated within a Docker container to ensure ease of portability and seamless integration within larger computational infrastructures such as Lunar rovers and autonomous robots. The application offers a full-fledged control interface for the Azure Kinect’s camera and sensor array through UNIX domain sockets and a novel capture sequence message schema. This paper explores the project’s driving force, objectives, and detailed technical implementation. It also underscores the application’s successful performance within NASA Ames Research Center’s Lunar regolith analog testing beds. Furthermore this paper discusses concepts of operation and deployment of the AKALL software on a Lunar Outpost’s Mobile Autonomous Prospecting Platform (MAPP) rover, which is set to conduct an extensive survey of the moon’s South Pole as part of NASA’s Commercial Lunar Payload Services (CLPS) program.