Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors

Deng, Fucheng; Zhu, Xiaorui; He, Chao

doi:10.3390/s17092101

Cited by 21 publications

(11 citation statements)

References 20 publications

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“…Visual perception elements of most field mobile robots such as LS3 [35], BigDog [36] and some wheeled robots [37,38] are located between 0.4 and 1.5 m from the horizontal plane when robots maintain upright postures. Considering that the terrain may be uneven or inclined, and the robot is undulating while walking, we extend the above range with a scale factor of 1.2.…”

Section: Fiti Datasetmentioning

confidence: 99%

A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain

et al. 2020

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The friction and stiffness properties of the terrain are very important pieces of information for mobile robots in motion control, dynamics parameter adjustment, trajectory planning, etc. Inferring the friction and stiffness properties in advance can improve the safety, adaptability and reliability, and reduce the energy consumption of the robot. This paper proposes a vision-based two-stage framework for pre-estimating physical properties of the terrain. We established a field terrain image dataset with weak annotations. A semantic segmentation network that can segment terrains at the pixel level was designed. Given that the same terrain also has different physical properties, we designed two kinds of image features, and we use a decision-making model to realize the mapping from terrain to physical properties. We trained and tested the network comprehensively, and experimented with the complete framework for estimating physical properties. The experimental results show that our framework has good performance.

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Section: Fiti Datasetmentioning

confidence: 99%

A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain

et al. 2020

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“…In this study, a vision sensor was used for road scene segmentation in environments where it is difficult to evaluate the traversability based on physical features alone. Several vision sensor-based traversable area segmentation methods are based on region growing [ 10 , 11 ]. In region growing methods, traversable areas are expanded by sequentially evaluating the similarity of the color space and texture of neighboring pixels or superpixels from the seed pixel or superpixel assumed to be traversable.…”

Section: Related Workmentioning

confidence: 99%

Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges

Onozuka

Matsumi

Shino

2021

Sensors

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Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

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“…A computer vision pipeline for traversable path detection from on-board images is proposed by Deng et al in [ 50 ]. The pipeline consist of estimating texture orientations, in order to estimate the vanishing point through line segment candidates voting, and finally constructing a multivariate Gaussian appearance model by selecting a sample region inside the traversable path.…”

Section: Terrain Traversability Analysismentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

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The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

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Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors

Cited by 21 publications

References 20 publications

A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain

A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain

Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

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