A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation
IEEE Transactions on Intelligent Vehicles(2023)
摘要
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 ours
with existing surveys. 2) Many well-known MRL methods are categorized and
reviewed into five classes according to the representation forms of utilized
map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized
semantic map, and neural network-based map. 3) To quantitatively and fairly
compare MRL methods with various map, we introduce some public datasets and
provide the performances of some state-of-the-art MRL methods. The strengths
and weakness of MRL methods with different map are analyzed. 4) We finally
introduce some topics of interest in this field and give personal opinions.
This survey can serve as a valuable referenced materials for MRL, and a
continuously updated summary of this survey is publicly available to the
community at: https://github.com/jinyummiao/map-in-mono-reloc.
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关键词
Simultaneous Localization and Mapping,Scene Map,Monocular Re-Localization,Pose Estimation
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