An End-to-End Learning-Based Row-Following System for an Agricultural Robot in Structured Apple Orchards

A row-following system based on end-to-end learning for an agricultural robot in an apple orchard was developed in this study. Instead of dividing the navigation into multiple traditional subtasks, the designed end-to-end learning method maps images from the camera directly to driving commands, which reduces the complexity of the navigation system. A sample collection method for network training was also proposed, by which the robot could automatically drive and collect data without an operator or remote control. No hand labeling of training samples is required. To improve the network generalization, methods such as batch normalization, dropout, data augmentation, and 10-fold cross-validation were adopted. In addition, internal representations of the network were analyzed, and row-following tests were carried out. Test results showed that the visual navigation system based on end-to-end learning could guide the robot by adjusting its posture according to different scenarios and successfully passing through the tree rows.

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Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

Remote Sensing ◽

10.3390/rs13040547 ◽

2021 ◽

Vol 13 (4) ◽

pp. 547

Author(s):

Wenning Wang ◽

Xuebin Liu ◽

Xuanqin Mou

Keyword(s):

Classification Accuracy ◽

Data Augmentation ◽

Classification Problem ◽

Classification Performance ◽

Spectral Structure ◽

Limited Sample ◽

Sample Classification ◽

Training Samples ◽

Traditional Classification ◽

Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

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GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

ACM Transactions on Sensor Networks ◽

10.1145/3466638 ◽

2022 ◽

Vol 18 (1) ◽

pp. 1-24

Author(s):

Yi Zhang ◽

Yue Zheng ◽

Guidong Zhang ◽

Kun Qian ◽

Chen Qian ◽

...

Keyword(s):

Data Augmentation ◽

Gait Recognition ◽

Wearable Sensors ◽

Human Identification ◽

Training Data ◽

Identification Accuracy ◽

Identification System ◽

Gait Patterns ◽

Training Samples ◽

Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

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MaxUp: Lightweight Adversarial Training with Data Augmentation Improves Neural Network Training

10.1109/cvpr46437.2021.00250 ◽

2021 ◽

Author(s):

Chengyue Gong ◽

Tongzheng Ren ◽

Mao Ye ◽

Qiang Liu

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Neural Network Training ◽

Network Training ◽

Adversarial Training

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Model-Based Visual Navigation System For A Mobile Robot

10.1117/12.949100 ◽

1989 ◽

Author(s):

Juha Roning ◽

Matti Pietikainen ◽

Mikko Lindholm ◽

Tapio Taipale

Keyword(s):

Mobile Robot ◽

Navigation System ◽

Visual Navigation ◽

Model Based

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Navigated Placement of Two Odontoid Screws Using the O-Arm Navigation System: A Technical Case Report

Cureus ◽

10.7759/cureus.10724 ◽

2020 ◽

Author(s):

Clara K Starkweather ◽

Ramin A Morshed ◽

Caleb Rutledge ◽

Phiroz Tarapore

Keyword(s):

Case Report ◽

Navigation System ◽

System A

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Development of a laboratory stand for simulation of UAV flight and training of a visual navigation system based on a convolutional neural network

Electronics and Control Systems ◽

10.18372/1990-5548.51.11689 ◽

2017 ◽

Vol 1 (51) ◽

Author(s):

V. M. Sineglazov ◽

V. S. Ischenko

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Navigation System ◽

Visual Navigation ◽

And Training

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Evaluating a Visual Navigation System for a Digital Library

Research and Advanced Technology for Digital Libraries - Lecture Notes in Computer Science ◽

10.1007/3-540-49653-x_32 ◽

1998 ◽

pp. 535-554 ◽

Cited By ~ 5

Author(s):

A. Leouski ◽

J. Allan

Keyword(s):

Digital Library ◽

Navigation System ◽

Visual Navigation

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Application of microwave motion sensors in navigation systems for ground vehicle

ITM Web of Conferences ◽

10.1051/itmconf/20193012004 ◽

2019 ◽

Vol 30 ◽

pp. 12004 ◽

Cited By ~ 1

Author(s):

Dmitrii Khablov

Keyword(s):

Navigation System ◽

Autonomous Navigation ◽

Velocity Vector ◽

Continuous Measurement ◽

Navigation Systems ◽

Motion Sensors ◽

Ground Vehicle ◽

Cumulative Error ◽

System A ◽

Transport Management

Effective land transport management in a controlled and unmanned mode is impossible without its accurate and continuous positioning. The paper discusses the possibility of increasing this accuracy in the absence or uncertain reception of signals from satellites of the global navigation system. Moreover, the use of an additional self-navigation inertial system to solve this problem in this case is not justified for reasons of accuracy and cost. Therefore, as an alternative autonomous navigation system, a solution based on radar Doppler sensors of modular type is proposed. The methods of measuring the velocity vector and the algorithm of direct continuous measurement of displacements are considered. It is shown that the latter measurement option can significantly reduce the cumulative error when positioning vehicles.

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