Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images

Ran, Lingyan; Zhang, Yanning; Zhang, Qilin; Yang, Tao

doi:10.3390/s17061341

Cited by 92 publications

(38 citation statements)

References 39 publications

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“…The convolutional neural network (CNN) is a prevalent type of deep learning algorithm, and it has a structure that can fully utilize two-dimensional input data. Thus, it has been widely applied to various image recognition fields such as motor fault diagnosis, vision-based mobile robot navigation, physiological signal analysis in medical assessment and human emotion recognition [15][16][17][18]. To our best knowledge, however, the CNN algorithm has not been used for research on the classification of sitting postures for children.…”

Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…The 360 o field of view of spherical images benefits many applications, such as autonomous driving [46], robotics [42] or VR [22]. Typically, omnidirectional images are modeled as a sphere, and its pixel coordinates map to the longitudinal and latitudinal spherical coordinates.…”

Section: Learning On 360 O Imagesmentioning

confidence: 99%

360° Surface Regression with a Hyper-Sphere Loss

Karakottas¹,

Zioulis²,

Samaras³

et al. 2019

2019 International Conference on 3D Vision (3DV)

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Omnidirectional vision is becoming increasingly relevant as more efficient 360 o image acquisition is now possible. However, the lack of annotated 360 o datasets has hindered the application of deep learning techniques on spherical content. This is further exaggerated on tasks where ground truth acquisition is difficult, such as monocular surface estimation. While recent research approaches on the 2D domain overcome this challenge by relying on generating normals from depth cues using RGB-D sensors, this is very difficult to apply on the spherical domain. In this work, we address the unavailability of sufficient 360 o ground truth normal data, by leveraging existing 3D datasets and remodelling them via rendering. We present a dataset of 360 o images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 o surface estimation. We achieve this by minimizing a novel angular loss function defined on the hyper-sphere using simple quaternion algebra. We put an effort to appropriately compare with other state of the art methods trained on planar datasets and finally, present the practical applicability of our trained model on a spherical image re-lighting task using completely unseen data by qualitatively showing the promising generalization ability of our dataset and model. The dataset is available at: vcl3d.github.io/ HyperSphereSurfaceRegression.

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“…We use a DNN for trail mapping from Google Earth images which is a solved problem ("Website" n.d.) (Ran et al 2017) (Ran et al 2017;"Website" n.d.)…”

Section: Trail Segmentationmentioning

confidence: 99%

Display Computing - Design of monocular Auto Pilot systems

Bheemaiah¹

2019

Preprint

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Display Computing is a new paradigm of GPU computing, where each image is computed from the previous images using generalized transformation theory. This is applied to monocular video for procedural and emergent A.I to create an autopilot for drones and robots. A microphone with the same A.I applied to audio infrasonic streams can supplement the autopilot system. This paper describes the mathematical models and algorithm

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Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images

Cited by 92 publications

References 39 publications

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

360° Surface Regression with a Hyper-Sphere Loss

Display Computing - Design of monocular Auto Pilot systems

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