FishNet for loop closure detection in VSLAM

Jian Zhou; Shuijie Wang; Yu Su; Yuhe Qiu; Qianqian Cheng

doi:10.1117/12.2659138

28 October 2022 FishNet for loop closure detection in VSLAM

Jian Zhou, Shuijie Wang, Yu Su, Yuhe Qiu, Qianqian Cheng

Proceedings Volume 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022); 1245312 (2022) https://doi.org/10.1117/12.2659138
Event: Third International Conference on Computer Communication and Network Security (CCNS 2022), 2022, Hohhot, China

Abstract

With the rapid development of drones, unmanned vehicles and robotics industries, VLAM has become a hot technology. In particular, the birth of 5G-powered UAV has promoted the emergence of more industrial applications, making it the most core and indispensable role in many scenarios. The loop closure detection can decrease the accumulative total of error during the process of VSLAM. Former loop closure detection methods always rely on artificially features, which are not robust, making it hard to deal with changing complex scenarios. The later deep learning-based methods are considered to be better solutions for loop closure detection. However, due to the simple network structure, there is still a lot of room for improvement. This paper proposes a more complex neural network to achieve loop closure detection. This approach adopts a fish-shaped deep neural network backbone, which can extract and fuse data features at different levels. Experiments demonstrate the feasibility and effectiveness of this backbone in loop closure detection problems.

Citation Download Citation

Jian Zhou, Shuijie Wang, Yu Su, Yuhe Qiu, and Qianqian Cheng "FishNet for loop closure detection in VSLAM", Proc. SPIE 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022), 1245312 (28 October 2022); https://doi.org/10.1117/12.2659138

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KEYWORDS

Head

Neural networks

Unmanned aerial vehicles

Detection and tracking algorithms

Evolutionary algorithms

Convolution

Feature extraction

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