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Yunpiao Cai, Weixing Qian, Jiaqi Zhao, Jiayi Dong and Tianxiao Shen
In this paper, we propose a novel visual?inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a ...
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Feihu Zhang, Diandian Xu and Chensheng Cheng
Multi-vehicle collaborative mapping proves more efficient in constructing maps in unfamiliar underwater environments in comparison to single-vehicle methods. One of the pivotal hurdles of Simultaneous Localization and Mapping (SLAM) with multiple underwa...
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Zheng Xu, Zhong Su and Dongyue Dai
In order to accurately locate personnel in underground spaces, positioning equipment is required to be mounted on wearable equipment. But the wearable inertial personnel positioning equipment moves with personnel and the phenomenon of measurement referen...
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Jesús Dassaef López-Barrios, Jesús Arturo Escobedo Cabello, Alfonso Gómez-Espinosa and Luis-Enrique Montoya-Cavero
In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of...
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Jianjun Ni, Li Wang, Xiaotian Wang and Guangyi Tang
The visual simultaneous localization and mapping (SLAM) method under dynamic environments is a hot and challenging issue in the robotic field. The oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one of the most effective methods. However, the tra...
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