Year: 2024
Type: conference-paper
Abstract: Indoor robot localization is a challenging problem in computer vision due to sensor obstacles in a crowded environment. Pure vision localization is increasingly popular since it does not require sensors other than low-cost cameras. We adopt a top-view camera setup, effectively avoiding the problem of positioning failure due to potential occlusion of front-view cameras. We use the distilling of a pre-trained large-scale vision language CLIP model to improve the performance degradation caused by the small data set size. Our solution achieved promising performance in our customized classification-based localization test data.
Authors: Aojie Zhao, Yifan Liu, Kun Cheng, Aiping Ma, Jianguo Yu
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G3678196313, G8194511106
OpenAlex ID: W4401517280