Fast initialization for feature-based monocular slam

Zhang, A. Shaobo; Liu, B. Sheng; Zhang, C. Jianhua; Wang, D. Zhenhua; Wang, E. Xiaoyan

doi:10.1109/icip.2017.8296656

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Then, epipolar geometry is used to estimate the scale based on the matched features between these frames. In another monocular SLAM approach [142], the depth of ORB features was computed based on their distance to the vanishing points identified in the scene. Furthermore, inverse depth parameterization was used in [26] to recover the scale of the scene.…”

Section: Resolving Scale Ambiguitymentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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Section: Resolving Scale Ambiguitymentioning

confidence: 99%