Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

Humblot-Renaux, Galadrielle; Marchegiani, Letizia; Moeslund, Thomas B.; Gade, Rikke

doi:10.1109/lra.2022.3144491

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…Scene understanding is one of the key tasks in robot semantic navigation, encompassing object detection and recognition, depth estimation, object tracking, and event classification. To achieve efficient robot semantic navigation, a mobile robot must comprehend both geometric and visual information [21]. Geometric-based navigation systems utilize distance measurement technologies.…”

Section: Indoor Robot Semantic Navigation Systemsmentioning

confidence: 99%

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Alqobali,

Alshmrani,

Alnasser

et al. 2023

Applied Sciences

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Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system.

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Section: Indoor Robot Semantic Navigation Systemsmentioning

confidence: 99%

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Alqobali,

Alshmrani,

Alnasser

et al. 2023

Applied Sciences

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“…Recent robot navigation trends have emerged to segment camera egocentric images using computer vision approaches such as visual attention mechanisms [105]. [106] presents a camera-only approach for segmenting egocentric images to assist robot navigation. The segmentation procedure consists of soft labelling the images according to three levels of driveability.…”

Section: Robot Scene Understanding For Navigation Planningmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga¹,

Rassau²,

Chai³

2023

Preprint

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The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, autonomous robots have been able to achieve relatively high levels of autonomy. In more unstructured environments, however, the realisation of autonomous mobile robots remains challenging due to limitations in the robots’ external environment understanding. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. The classical navigation approach typically includes robot perception, localisation, environmental mapping, path planning and motion control stages. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots need to be able to understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, and rough or smooth terrain) are required for autonomous robot navigation in unstructured outdoor environments. A wide number of alternative approaches have been proposed in recent years to attempt to address these scene understanding requirements. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments, and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

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“…Ref. [109] presents a camera-only approach for segmenting egocentric images to assist robot navigation. The segmentation procedure consists of soft labelling of the images according to three levels of driveability.…”

Section: Robot Scene Understanding For Navigation Planningmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga,

Rassau,

Chai

2023

Applied Sciences

View full text Add to dashboard Cite

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

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Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

Cited by 16 publications

References 37 publications

A Survey on Robot Semantic Navigation Systems for Indoor Environments

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

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