Absolute Localization Through Orbital Maps and Surface Perspective Imagery: A Synthetic Lunar Dataset and Neural Network Approach We present a neural network approach and publicly available dataset for developing and benchmarking algorithms for localization on the Moon. Accurate localization is essential for navigation, path planning, and science objectives. On Earth, localization can be achieved using a satellite navigation system; such a system is, however, unavailable for other planetary bodies. Current absolute and relative localization methods for rovers on planetary surfaces are laborious manual tasks or accumulate significant location errors over time resulting in incorrect location estimates, respectively. Recently, Earthbased approaches have explored localization by matching surfaceperspective imagery to satellite imagery using computer vision and machine learning. Those focusing on natural environments have shown such image matching to be feasible for localization, though it remains challenging. The purpose of this project was to produce a working proofofconcept model to match surfaceperspective imagery to satellite images for planetary roving applications. This would improve absolute localization accuracy (relative to current methods) by utilizing machine learning algorithms to offer greater automation and faster calculations, and produce a benchmark dataset for future studies. To achieve these, the following objectives were met: (1) configuration and modification of an existing synthetic planetary environment using Unreal Engine 4 to generate surface and satellite perspective images, (2) reprojection of 2.4  million surface perspective images to 600,000  satellite perspective images, and (3) matching of said reprojected images to satellite images via a Siamese convolutional neural network, designed and trained for this task.

Improved Planetary Rover Inertial Navigation and Wheel Odometry Performance through Periodic Use of ZeroType Constraints We present an approach to enhance wheeled planetary rover deadreckoning localization performance by leveraging the use of zerotype constraint equations in the navigation filter. Without external aiding, inertial navigation solutions inherently exhibit cubic error growth. Furthermore, for planetary rovers that are traversing diverse types of terrain, wheel odometry is often unreliable for use in localization, due to wheel slippage. For current Mars rovers, computer visionbased approaches are generally used whenever there is a high possibility of positioning error; however, these strategies require additional computational power, energy resources, adequate features in the environment, and significantly slow down the rover traverse speed. To this end, we propose a navigation approach that compensates for the high likelihood of odometry errors by providing a reliable navigation solution that leverages nonholonomic vehicle constraints as well as stateaware pseudomeasurements (e.g., zero velocity and zero angular rate) updates during periodic stops. By using this, computationally expensive visualbased corrections could be performed less often. Experimental tests that compare against GPSbased localization are used to demonstrate the accuracy of the proposed approach. The source code, postprocessing scripts, and example datasets associated with the paper are published in a public repository.