Research on AGV Navigation System Based on Binocular Vision

Ai, Changsheng; Geng, Dunyang; Qi, Zhengguang; Zheng, Lei; Feng, Zhiquan

doi:10.1109/rcar52367.2021.9517359

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The advantages of the monocular camera include a simple structure, low cost, and low power consumption, making it a convenient tool for crop row detection. Additionally, monocular cameras can detect color, texture, and other features in agricultural scenes, providing useful information for the positioning and navigation of agricultural robots and vehicles [24]. However, due to the limited amount of information provided by a single camera, auxiliary algorithms are often required to determine the distance of the relationship between the target and the camera [25,26].…”

Section: Monocular Camerasmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

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Section: Monocular Camerasmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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“…Ma 7 developed a control system that integrated satellite navigation and visual navigation using a monocular camera and a satellite module. Ai 8 proposed an AGV navigation method using a stereo camera to identify the lane lines, obtain the positional deviation of the body relative to the lane lines, and track the path. Chen 9 proposed a new method for the online self-motion estimation of combined harvesters using stereo cameras.…”

Section: Introductionmentioning

confidence: 99%

DeeplabV3+-based navigation line extraction for the sunlight robust combine harvester

Cheng,

Jin,

Chen

2024

Science Progress

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Visual navigation is widely used in intelligent combine harvesters, but the existing algorithms do not have sufficiently high accuracy of the visual navigation line recognition under different sunlight conditions. To address this problem, this article proposes a sunlight-robust DeepLabV3+-based navigation line extraction method for combine harvesters. The navigation lines are extracted by constructing a new dataset and predicting the boundaries of the areas that have been and have not been cut. To address the problem that DeeplabV3+ is not sufficient light in the DCNN part, improvement is proposed by incorporating the MobileNetV2 module. In image segmentation, the prediction time is 22.5 ms, and the mean intersection over union ( FMIOU) is 0.79. After image segmentation, the navigation lines are drawn using the line segment detection algorithm for the harvester. The proposed method is compared with other mainstream networks, and the prediction results are compared using the line segment detection method. The results show that this method can more quickly identify the navigation lines under different conditions of sunlight with less labeled data than the improved U-Net and DeeplabV3+, which uses Xception as the backbone. Compared to the traditional method and the improved U-Net, this method achieves good results and improves the recognition speed by 27 and 9 ms, respectively.

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