A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation
Jinyu Miao, Kun Jiang, Tuopu Wen, Yunlong Wang, Peijing Jia, Xuhe, Zhao, Qian Cheng, Zhongyang Xiao, Jin Huang, Zhihua Zhong, Diange Yang

TL;DR
This survey comprehensively reviews monocular re-localization methods focusing on how different scene map representations impact their performance, providing a systematic categorization, comparison, and analysis of recent techniques and datasets.
Contribution
It uniquely categorizes MRL methods based on scene map representations and offers a comparative analysis with performance evaluations and insights for future research.
Findings
Different map representations significantly affect localization accuracy.
Neural network-based maps show promising robustness and efficiency.
Public datasets enable fair comparison of MRL methods.
Abstract
Monocular Re-Localization (MRL) is a critical component in autonomous applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map based on monocular images. In recent decades, significant progress has been made in the development of MRL techniques. Numerous algorithms have accomplished extraordinary success in terms of localization accuracy and robustness. In MRL, scene maps are represented in various forms, and they determine how MRL methods work and how MRL methods perform. However, to the best of our knowledge, existing surveys do not provide systematic reviews about the relationship between MRL solutions and their used scene map representation. This survey fills the gap by comprehensively reviewing MRL methods from such a perspective, promoting further research. 1) We commence by delving into the problem definition of MRL, exploring current challenges, and comparing…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Robotic Path Planning Algorithms
