Training Semantic Descriptors for Image-Based Localization
- URL: http://arxiv.org/abs/2202.01212v1
- Date: Wed, 2 Feb 2022 12:17:59 GMT
- Title: Training Semantic Descriptors for Image-Based Localization
- Authors: Ibrahim Cinaroglu and Yalin Bastanlar
- Abstract summary: We show that localization can be performed via descriptors solely extracted from semantically segmented images.
Experiments reveal that the localization performance of a semantic descriptor can increase up to the level of state-of-the-art RGB image based methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Vision based solutions for the localization of vehicles have become popular
recently. We employ an image retrieval based visual localization approach. The
database images are kept with GPS coordinates and the location of the retrieved
database image serves as an approximate position of the query image. We show
that localization can be performed via descriptors solely extracted from
semantically segmented images. It is reliable especially when the environment
is subjected to severe illumination and seasonal changes. Our experiments
reveal that the localization performance of a semantic descriptor can increase
up to the level of state-of-the-art RGB image based methods.
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