Visual Loop Closure Detection Based on SqueezeNet Multi-layer Feature Fusion and Adaptive Range Matching Algorithm-Reference-Cited by-同舟云学术

Visual Loop Closure Detection Based on SqueezeNet Multi-layer Feature Fusion and Adaptive Range Matching Algorithm

Published:2023-07 Issue:3 Volume:108 Page:
ISSN:0921-0296
Container-title:Journal of Intelligent & Robotic Systems
language:en
Short-container-title:J Intell Robot Syst

Author:

Hu Zhengnan,Hu Likun^ORCID

Funder

Guangxi Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Industrial and Manufacturing Engineering,Mechanical Engineering,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10846-023-01912-4.pdf

Reference39 articles.

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2. Han, J., Kim, J., Shim, D.H.: Precise localization and mapping in indoor parking structures via parameterized slam. IEEE Trans. Intell. Transp. Syst. 20(12), 4415–4426 (2018)

3. Xu, Y., Huang, J., Wang, J., Wang, Y., Qin, H., Nan, K.: Esa-vlad: a lightweight network based on second-order attention and netvlad for loop closure detection. IEEE Robot. Autom. Lett. 6(4), 6545–6552 (2021)

4. Wang, H., Wang, C., Xie, L.: Intensity scan context: Coding intensity and geometry relations for loop closure detection. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2095–2101 (2020). IEEE

5. Ying, T., Yan, H., Li, Z., Shi, K., Feng, X.: Loop closure detection based on image covariance matrix matching for visual slam. Int. J. Control Autom. Syst. 19(11), 3708–3719 (2021)