Machine Learning For Localization
Machine Learning for Localization is a computational methodology that employs data-driven models to infer spatial positions within indoor environments by learning patterns from sensor observations, distinguished from conventional approaches through its reliance on adaptive algorithms that generalize from training data rather than fixed physical or geometrical assumptions.
762
Publications
36.1K
Citations
2.5K
Authors
811
Institutions
Publications per year
2017–2026
521
Parents
762
Image-Based Localization Using LSTMs for Structured Feature Correlation
Florian Walch, Laura Leal-Taixé, Torsten Sattler et al. · 2017 · 520 citations
1–5 of 762
2.5K
Hong Kong University of Science and Technology, Hong Kong SAR China
12 publications · 1.2K citations
12
h-index
8
h-index
7
h-index
7
h-index
Egypt-Japan University of Science and Technology, Egypt
7 publications · 340 citations
7
h-index
1–5 of 2.5K
811
Nanyang Technological University
Singapore, Singapore
15 h-index
48 publications
2.7K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
14 h-index
40 publications
3.4K citations
University of Oxford
Oxford, United Kingdom
13 h-index
43 publications
7.3K citations

Chinese Academy of Sciences
Beijing, China
12 h-index
25 publications
1.1K citations

University of Science and Technology Beijing
Beijing, China
11 h-index
31 publications
1.1K citations
Wuhan University
Wuhan, China
11 h-index
39 publications
2.1K citations

Beijing University of Posts and Telecommunications
Beijing, China
10 h-index
28 publications
3.8K citations
Seoul National University
Seoul, South Korea
10 h-index
27 publications
858 citations
1–8 of 811
59 publications · 2.3K citations
30
h-index
35 publications · 2.2K citations
27
h-index
27 publications · 1.1K citations
20
h-index
18 publications · 731 citations
16
h-index
12 publications · 423 citations
12
h-index
1–5