Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

Betoño, Daniel Teso Fernández de; Zulueta, Ekaitz; Sánchez-Chica, Ander; Fernandez-Gamiz, Unai; Aguirre, A.

doi:10.3390/math8050855

Cited by 33 publications

(15 citation statements)

References 33 publications

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“…In this way, the parameters of the deep Q-network are trained, solving the path planning problem, as Zheng et al [41] demonstrated. Another option is to implement a convolutional neural network (CNN) that segments an image to condition the navigation zone, proposed by Teso-Fz-Betoño et al [42]. The study manages a residual neural network that participates in the learning of the Resnet-18 network.…”

Section: Path Planningmentioning

confidence: 99%

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Dynamical Analysis of a Navigation Algorithm

et al. 2021

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There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

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Section: Path Planningmentioning

confidence: 99%

“…To obtain the trajectory, the example of the study by Teso-Fz-Betoño et al [42] is followed. A convolutional neural network (CNN) is managed to perform semantic segmentation of an image.…”

Section: Necessary Data Acquisitionmentioning

confidence: 99%

Dynamical Analysis of a Navigation Algorithm

et al. 2021

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“…In terms of time consumption, previous research indicated that some models with higher classification accuracy consume more system resources and require more calculation time [41,42]. Take SegNet, which is good at semantic image segmentation, for example.…”

Section: Comprehensive Evaluation Of Model Application Efficiencymentioning

confidence: 99%

Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification

Lin

Chuang

2021

Remote Sensing

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When original aerial photographs are combined with deep learning to classify forest vegetation cover, these photographs are often hindered by the interlaced composition of complex backgrounds and vegetation types as well as the influence of different deep learning calculation processes, resulting in unpredictable training and test results. The purpose of this research is to evaluate (1) data preprocessing, (2) the number of classification targets, and (3) convolutional neural network (CNN) approaches combined with deep learning’s effects on high-resolution aerial photographs to identify forest and vegetation types. Data preprocessing is mainly composed of principal component analysis and content simplification (noise elimination). The number of classification targets is divided into 14 types of forest vegetation that are more complex and difficult to distinguish and seven types of forest vegetation that are simpler. We used CNN approaches to compare three CNN architectures: VGG19, ResNet50, and SegNet. This study found that the models had the best execution efficiency and classification accuracy after data preprocessing using principal component analysis. However, an increase in the number of classification targets significantly reduced the classification accuracy. The algorithm analysis showed that VGG19 achieved the best classification accuracy, but SegNet achieved the best performance and overall stability of relative convergence. This proves that data preprocessing helps identify forest and plant categories in aerial photographs with complex backgrounds. If combined with the appropriate CNN algorithm, these architectures will have great potential to replace high-cost on-site forestland surveys. At the end of this study, a user-friendly classification system for practical application is proposed, and its testing showed good results.

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“…An ANN consists of many interconnected simple functional units (neurons) that perform as parallel information-processors and approximate the function that maps inputs to outputs [33]. ANNs potential to solve problems with high performance and the ability to adapt to different problems have been implemented in numerous fields such as autonomous driving [34,35], solar and wind energy systems [36,37] and financial time series forecasting [38]. The field of ANNs is in full motion, in that way to review its progress and application areas in real-world scenarios, see Abiodun et al [39].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

et al. 2021

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Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

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Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

Cited by 33 publications

References 33 publications

Dynamical Analysis of a Navigation Algorithm

Dynamical Analysis of a Navigation Algorithm

Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

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