Traversable Region Estimation for Mobile Robots in an Outdoor Image

Matsuzaki, Sango; Yamazaki, Kimitoshi; Hara, Yoshitaka; Tsubouchi, Takashi

doi:10.1007/s10846-017-0760-x

Cited by 13 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other methods exploit object semantics estimated from RGB images [20,21,22,23,24,25]. The rich information of RGB images such as colors and texture allows for estimating object semantics such as object class and traversability from the appearance.…”

Section: ) Semantics-based Methodsmentioning

confidence: 99%

“…Zhou et al [20] trained a terrain classifier on visual features with class labels automatically given using structural information from a 3D LiDAR. Matsuzaki et al [21] used examples of traversable path lines given by human annotators to train a probabilistic model to classify the image regions into either of the object classes: building, road, or grass. In the last decade, DNNs played a crucial role to provide high recognition accuracy and generalization ability.…”

Section: ) Semantics-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Image-Based Scene Recognition for Robot Navigation Considering Traversable Plants and Its Manual Annotation-Free Training

2022

View full text Add to dashboard Cite

Matsuzaki et al.: Scene recognition for robot navigation considering traversable plants and manual annotation-free training This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

show abstract

Section: ) Semantics-based Methodsmentioning

confidence: 99%

Section: ) Semantics-based Methodsmentioning

confidence: 99%

Image-Based Scene Recognition for Robot Navigation Considering Traversable Plants and Its Manual Annotation-Free Training

2022

View full text Add to dashboard Cite

show abstract

“…The traversable area detection and obstacle avoidance are two sides of the same coin. While traversable area detection determines the area where a user can walk safely [ 130 , 131 , 132 ], an obstacle avoidance task detects the location of obstacles and assists the user in avoiding them [ 25 ].…”

Section: Real-time Navigationmentioning

confidence: 99%

“…BVIP need to know more than simply where the traversable area of a sidewalk is [ 130 , 131 , 132 ]. While the ground may be empty and traversable, there may be other kinds of obstacles that prevent walking safely, such as head, chest, and knee level obstacles.…”

Section: Real-time Navigationmentioning

confidence: 99%

A Systematic Review of Urban Navigation Systems for Visually Impaired People

El-taher

Taha

Courtney

et al. 2021

Sensors

View full text Add to dashboard Cite

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.

show abstract

“…There are many vision-based methods proposed for various perception tasks. For example, [17] use SIFT features extracted from the RGB images and classify terrains using simple models like MLP. In recent years, as many fast and reliable deep learning solutions for computer vision have been developed, researchers have begun to train a deep model for perception using a large amount of annotated data.…”

Section: B Perception In Unknown Environmentsmentioning

confidence: 99%

TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

Guan¹,

He²,

Song³

et al. 2021

Preprint

View full text Add to dashboard Cite

We present Terrain Traversability Mapping (TTM), a real-time mapping approach for terrain traversability estimation and path planning for autonomous excavators in an unstructured environment. We propose an efficient learningbased geometric method to extract terrain features from RGB images and 3D pointclouds and incorporate them into a global map for planning and navigation for autonomous excavation. Our method used the physical characteristics of the excavator, including maximum climbing degree and other machine specifications, to determine the traversable area. Our method can adapt to changing environments and update the terrain information in real-time. Moreover, we prepare a novel dataset, Autonomous Excavator Terrain (AET) dataset, consisting of RGB images from construction sites with seven categories according to navigability. We integrate our mapping approach with planning and control modules in an autonomous excavator navigation system, which outperforms previous method by 49.3% in terms of success rate based on existing planning schemes. With our mapping the excavator can navigate through unstructured environments consisting of deep pits, steep hills, rock piles, and other complex terrain features.

show abstract

Traversable Region Estimation for Mobile Robots in an Outdoor Image

Cited by 13 publications

References 18 publications

Image-Based Scene Recognition for Robot Navigation Considering Traversable Plants and Its Manual Annotation-Free Training

Image-Based Scene Recognition for Robot Navigation Considering Traversable Plants and Its Manual Annotation-Free Training

A Systematic Review of Urban Navigation Systems for Visually Impaired People

TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

Contact Info

Product

Resources

About