Occupancy grid mapping for rover navigation based on semantic segmentation

Chiodini, Sebastiano; Pertile, Marco; Debei, S.

doi:10.21014/acta_imeko.v10i4.1144

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…The results showed that the model presented good results in the detection of t-shirts with an average precision of 95 %. Furthermore, several studies [34]- [37] presented methods to segment objects using CNNs which have similarities to the implementation used and modelized for the garment segmentation and classification. Table 2 summarizes the main results of the segmentation and classification methods analysed.…”

Section: Solution Descriptionmentioning

confidence: 99%

Automatic system to identify and manage garments for blind people

Silva,

Rocha,

Soares

et al. 2023

Acta IMEKO

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In recent years, there has been increased attention towards the integration of handicapped individuals in society, with significant efforts being made to promote their inclusion. Technology has played a critical role in this effort, with several technological solutions emerging to help handicapped people in their daily routines, enabling them to better integrate into society. However, there are still challenges that remain, particularly in the area of basic tasks for blind people, such as managing and identifying personal garments. This study seeks to provide an improvement in the quality of life and well-being of blind people. A mechatronic automatism that allows the identification of user garments using sensors was developed. An interface developed with the implementation of a server responsible for managing the requests from the user is integrated. Algorithms were implemented for segmentation and classification of garments and for detecting the predominant colors of each garment. By the results obtained it was evidenced that the system could be an efficient solution to reduce the time taken for garment selection, particularly in terms of color differentiation and the selection of combinations for blind people.

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Section: Solution Descriptionmentioning

confidence: 99%

Automatic system to identify and manage garments for blind people

Silva,

Rocha,

Soares

et al. 2023

Acta IMEKO

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“…In [37], a terrain segmentation model is proposed using PSPNet [38], trained by real rover-based images from Mars and artificial images generated by the Unity3D software, aiming to automate a path planning algorithm on the Martian surface. In [39], authors propose a methodology for path rerouting using imagery data, depth maps and a CNN-based neural network trained with Katwijk beach planetery rover dataset, in order to detect and avoid obstacles such as rocks and boulders.…”

Section: Introductionmentioning

confidence: 99%

Lunar ground segmentation using a modified U-net neural network

Petrakis,

Partsinevelos

2024

Machine Vision and Applications

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Semantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders. It achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a safer and smoother navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, aiming to reinforce the autonomous rover navigation.

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“…A crucial use of terrain classification in planetary environments is the path planning optimization [36]. In [37], a deep learning model for terrain segmentation is proposed using PSPNet [38] model, trained by real rover-based images from Mars and artificial images generated by the Unity3D software, aiming to automate a path planning algorithm on the Martian surface while in [39], authors propose a methodology for path rerouting using imagery data, depth maps and a CNN-based neural network trained with Katwijk beach planetery rover dataset, aiming to detect and avoid obstacles such as rocks and boulders.…”

Section: Introductionmentioning

confidence: 99%

Lunar Ground Segmentation Using a Modified U-Net Neural Network

Petrakis,

Partsinevelos

2023

Preprint

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Semantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders while it achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a more safe and smooth navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, focused on rover navigation.

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Occupancy grid mapping for rover navigation based on semantic segmentation

Cited by 6 publications

References 27 publications

Automatic system to identify and manage garments for blind people

Automatic system to identify and manage garments for blind people

Lunar ground segmentation using a modified U-net neural network

Lunar Ground Segmentation Using a Modified U-Net Neural Network

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