A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation

Monocular Re-Localization (MRL) is an essential component in autonomous applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map using monocular images. Over the past few decades, there has been notable advancement in the field of MRL techniques. Several works have achieved remarkable success in terms of both the accuracy and reliability of localization. In MRL, scene maps are represented in diverse forms, and they dictate the functionality and efficacy of MRL methods. However, to the best of our knowledge, existing surveys do not provide systematic reviews about the correlation 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) MRL methods are categorized and reviewed into five classes based on the representation forms of the utilized map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized semantic maps, and neural network-based maps. 3) To quantitatively and fairly compare MRL methods using different maps, we introduce some public datasets and evaluate the performances of several advanced MRL approaches. The strengths and shortcomings of MRL methods using various maps are analyzed. 4) Finally, we introduce some topics of interest in this field and provide our individual viewpoints. This survey can serve as a valuable resource for MRL, and a summary of this survey is accessible to the community at:
github.com/jinyummiao/map-in-mono-reloc