Enabling Long & Precise Drives for The Perseverance Mars Rover via Onboard Global Localization

2024 IEEE Aerospace Conference(2024)

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摘要
The Perseverance Mars rover needs to drive long distances between regions of scientific interest to collect a diverse set of samples. Position knowledge is needed for navigating to the region of interest. Planetary mobile robots accumulate position uncertainty as they move. Globally localizing the robot to an orbital map of Mars removes this uncertainty. To date, this has been performed manually on the ground by humans for mobile surface and aerial robots. This can be accurate but requires communication between planets. This takes significant time and the need for it limits how far Perseverance can autonomously navigate without ground-in-the-loop.This paper describes a new onboard approach for performing global localization, much of which already has been successfully demonstrated on Perseverance. Our Censible technology uses a modified census transform to achieve sub-meter global localization accuracy that is robust and practical, and whose performance matches human-directed localizations from the first two and a half years of the mission to within 0.5 meters on average with no outliers. We use the fast processor on the Ingenuity Helicopter Base Station mounted in the Perseverance rover to perform the localization. It was originally installed to coordinate communication with Ingenuity. This effort developed the interfaces and radiation mitigation methods needed to enable its use as a rover co-processor. The system is designed to limit operations impact and requires no daily input from rover operators other than whether or not to perform global localization, but also allows strategic configuration options if desired. We discuss the lessons learned from developing and deploying this new technology on a flight mission, and describe how global localization is expected to increase science return and change how planetary mobile robots navigate.
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关键词
Global Localization,Navigation,Base Station,Position Uncertainty,Robot Navigation,Autonomous Navigation,Rank Transformation,Satellite,Digital Elevation Model,Point Cloud,Benchmark Datasets,Human Experts,Position Estimation,Background Radiation,Thermal Environment,File Size,Antirrhinum,Future Missions,Sum Of Squared Differences,Visual Odometry,Stereo Pairs,Normalized Cross-correlation,File System,Lossless Compression,Orbital Imaging,Computational Elements,Dangerous Areas,Late Morning,Software Updates
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