Abstract
In Robotics, autonomous navigation has been addressed in recent years due to the potential of applications in different areas, such as industrial, comercial, health and entertainment. The capacity to navigate, whether autonomous vehicles or service robots, is related to the problem of Simultaneous Localization And Mapping (SLAM). Loop closure, in the context of Visual SLAM, uses information from the images to identify previously visited environments, which allows for correcting and updating the map and the robot’s localization. This paper presents a system that identifies loop closure and uses a Convolutional Neural Network (CNN) trained in Gazebo simulated environment. Based on the concept of transfer learning, the CNN of VGG-16 architecture is retrained with images from a scenario in Gazebo to enhance the accuracy of feature extraction. This approach allows for the reduction of the descriptors’ dimension. The features from the images are captured in real-time by the robot’s camera, and its control is performed by the Robot Operating System (ROS). Furthermore, loop closure is addressed from image preprocessing and its division in the right and left regions to generate the descriptors. Distance thresholds and sequences are defined to enhance performance during image-to-image matching. A virtual office designed in Gazebo was used to evaluate the proposed system. In this scenario, loop closures were identified while the robot navigated through the environment. Therefore, the results showed good accuracy and a few false negative cases.
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Acknowledgements
This work was supported by a Technical Training Fellowship (TT-3/Process Number: 2019/12080-5) funded by the São Paulo Research Foundation (FAPESP)/PIPE Grant Program from July/2019 to December/2020 offered by the Startup NTU Software Technology (Process Number: 2018/04306-0).
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Iegawa, F.N., Botelho, W.T., Santos, T.d., Pimentel, E.P., Yamamoto, F.S. (2023). Loop Closure Detection in Visual SLAM Based on Convolutional Neural Network. In: Latifi, S. (eds) ITNG 2023 20th International Conference on Information Technology-New Generations. ITNG 2023. Advances in Intelligent Systems and Computing, vol 1445. Springer, Cham. https://doi.org/10.1007/978-3-031-28332-1_1
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