Relocalization under Substantial Appearance Changes using Hashing
نویسندگان
چکیده
Localization under appearance changes is essential for robots during long-term operation. This paper investigates the problem of place recognition in environments that undergo dramatic visual changes. Our approach builds upon previous work on graph-based image sequence matching and extends it by incorporating a hashing-based image retrieval strategy in case of localization failures or the kidnapped robot problem. We present a variant of hashing algorithm that allows for fast retrieval for high-dimensional CNN features. Our experiments suggest that our algorithm can reliably recover from localization errors by globally relocalizing the robot. At the same time, our hashing-based candidate selection is substantially faster than state-of-the-art locality sensitive hashing.
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