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Deep Visual Odometry with Adaptive Memory
[article]
2020
arXiv
pre-print
We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive
arXiv:2008.01655v1
fatcat:oivwiy5oyrgntkry46awtwyvlq