A robot vision navigation method using deep learning in edge computing environment

Li, Jing; Yin, Jie; Deng, Lin

doi:10.1186/s13634-021-00734-6

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…Instead, it models the target and estimates its state to obtain target information. Obtain target tracking information [13]. Commonly used filtering methods are Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and so on.…”

Section: Introductionmentioning

confidence: 99%

Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

Huang

Zhang

2022

International Journal of Antennas and Propagation

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In order to improve the navigation control effect of indoor mobile robot, this paper studies the navigation control method of indoor mobile robot combined with visual servo technology. For the problem that the target may be out of the camera’s field of view during the servo process, a control algorithm with field-of-view constraints is proposed, which assigns a certain weight to the image feature error at each moment. Moreover, this paper realizes the servo process that the image features are always located in the field of view by setting the variable values involved in the weight value calculation method. Through the simulation test, it is verified that the indoor mobile robot navigation control method based on visual servo has certain effects.

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Section: Introductionmentioning

confidence: 99%

Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

Huang

Zhang

2022

International Journal of Antennas and Propagation

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“…ere is a relationship between the golden ratio; the second is that the three colors cannot be the same; otherwise, it will easily cause the monotony of the space color and even produce a blurred feeling of the surface painting; third, the environment color and the intermediate color should maintain a harmonious relationship; decoration color and ambient color should have both contrast and coordination in color relationship [13].…”

Section: Related Workmentioning

confidence: 99%

Color Matching in Children’s Room Based on Computer Interactive Experience

Huang

2022

Mathematical Problems in Engineering

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In order to improve the effect of color matching in children’s room, this paper studies the color matching of children’s room combined with computer interactive experience technology. Moreover, this paper analyzes the basic knowledge of colorimetry, the principle of color measurement equipment, the basic concepts and mathematical models of color conversion, equipment color characteristic correction, and color gamut matching used in color management research and analyzes the color perception in the process of computer interaction experience. The color design method based on hue harmony is a color harmony design method extracted on the basis of the basic color system and Chevrel’s theory of color harmony. The experimental research shows that the indoor color matching system for children’s room based on computer interactive experience proposed in this paper has a good color matching effect.

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“…CNNs are one of the most widely used models in deep learning [7], and are able to achieve state-of-the-art results in image analysis applications of different areas, i.e., image forensic [8], sea ice detection [9], autonomous navigation [10], and agriculture [11]. They represent a fully automatic way to address the problem of oil spill identification since they do not require a human to define the specific features that are used to classify oil spill and look-alike formations.…”

Section: Related Workmentioning

confidence: 99%

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

Diana

Fanucci

2021

Remote Sensing

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Oil spills represent one of the major threats to marine ecosystems. Satellite synthetic-aperture radar (SAR) sensors have been widely used to identify oil spills due to their ability to provide high resolution images during day and night under all weather conditions. In recent years, the use of artificial intelligence (AI) systems, especially convolutional neural networks (CNNs), have led to many important improvements in performing this task. However, most of the previous solutions to this problem have focused on obtaining the best performance under the assumption that there are no constraints on the amount of hardware resources being used. For this reason, the amounts of hardware resources such as memory and power consumption required by previous solutions make them unsuitable for remote embedded systems such as nano and micro-satellites, which usually have very limited hardware capability and very strict limits on power consumption. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. It also provides many additional very significant advantages, such as having shorter inference times, requiring shorter training times, and avoiding transmission of irrelevant data. Our goal is to allow embedded low power remote devices such as satellite systems for remote sensing to be able to directly run CNNs on board, so that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR images.

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A robot vision navigation method using deep learning in edge computing environment

Cited by 18 publications

References 15 publications

Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

Color Matching in Children’s Room Based on Computer Interactive Experience

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

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