June 10, 2024, 4:48 a.m. | Matthew Rodda, Sofia McLeod, Ky Cuong Pham, Tat-Jun Chin

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.04569v1 Announce Type: new
Abstract: As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact craters visible through onboard imagery with a known database to estimate a craft's pose. However, existing literature has not sufficiently evaluated crater-detection algorithm (CDA) performance from imagery containing off-nadir view angles. In this work, we evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained …

abstract aim arxiv craft cs.cv database detection explore however impact literature navigation robust safe space space missions through type vision

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