(Georgia Institute of Technology, 2023-08)
Thrasher, Ava C.; Christian, John A.; Molina, Giovanni; Hansen, Michael; Pelgrift, John Y.; Nelson, Derek S.
Image-based terrain relative navigation is a critical capability for future lunar exploration
missions. Images of the lunar surface containing craters can be compared
to on-board maps to identify craters and estimate the spacecraft position. While
there are many ways to accomplish the crater identification task, this work explores
a method using triangulation and crater triangle pattern projections. Specifically, potential
matching crater patterns from the catalog and image are used to triangulate
the spacecraft position, allowing for construction of line-of-sight directions to the
potential matching catalog craters. The projection of these directions in the image
can be compared to the observed craters to accept or reject the match hypothesis.
In this paper, we demonstrate the algorithm's capability in handling various types
of input errors and what tolerances can be tuned to achieve a desired performance.
Additionally, an initial look at flight software implementation is included.
(Georgia Institute of Technology, 2022-08)
DeVries, Carl; Christian, John A.; Hansen, Michael; Crain, Tim
Future missions to the lunar surface are expected to make use of LIDAR sensors
for navigation during landing. This is especially true when the lunar landing
must be accomplished under lighting conditions that are undesirable for camera based
navigation. Moreover, when local maps of the lunar terrain are also poor or
unavailable to the lander in real-time, concepts from LIDAR odometry (LO) are
highly relevant. This work develops an algorithmic framework for LO suitable for
supporting future lunar exploration missions.
(Georgia Institute of Technology, 2022-02)
Molina, Giovanni; Hansen, Michael; Getchius, Joel; Christensen, Randall; Christian, John A.; Stewart, Shaun; Crain, Tim
Intuitive Machines has developed a state-of-the-art precision landing and hazard
avoidance (PLHA) system that will enable its Nova-C lunar lander to safely touch
down on the lunar surface. This system consists of an array of sensors that includes
an inertial measurement unit (IMU), optical camera, and a laser range finder sensor
(LRFS) as well a series of algorithms which process and fuse the sensor data and
produce measurements used in the navigation system. One of the measurements
utilizes image processing and visual odometry (VO) to compute a delta-position
(DPOS) measurement which describes the lander's direction of motion between
two image captures. In this paper, we detail the development and implementation
of this measurement for the Nova-C lander, demonstrate our rigorous testing
methodologies and present our findings and results.