Non-contact terrain classification for autonomous mobile robot

Kim, Jayoung; Kim, Doo-Gyu; Lee, Jong-Hwa; Lee, Jihong; Joo, Hanbyul; Kweon, In-So

doi:10.1109/robio.2009.5420568

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Furthermore they use a hidden semi Markov model for the class distributions in vertical cell columns. Other research has been conducted on robust terrain classification for mobile robots [13], [14], [15] which could be leveraged to predict vegetation with discrete height values. However, this work presents a support surface estimation system that does not rely on classification but rather regresses the height estimate directly from sensor measurements.…”

Section: B Related Workmentioning

confidence: 99%

Support Surface Estimation for Legged Robots

Homberger

Wellhausen

Fankhauser

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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The high agility of legged systems allows them to operate in rugged outdoor environments. In these situations, knowledge about the terrain geometry is key for foothold planning to enable safe locomotion. However, on penetrable or highly compliant terrain (e.g. grass) the visibility of the supporting ground surface is obstructed, i.e. it cannot directly be perceived by depth sensors. We present a method to estimate the underlying terrain topography by fusing haptic information about foot contact closure locations with exteroceptive sensing. To obtain a dense support surface estimate from sparsely sampled footholds we apply Gaussian process regression. Exteroceptive information is integrated into the support surface estimation procedure by estimating the height of the penetrable surface layer from discrete penetration depth measurements at the footholds. The method is designed such that it provides a continuous support surface estimate even if there is only partial exteroceptive information available due to shadowing effects. Field experiments with the quadrupedal robot ANYmal show how the robot can smoothly and safely navigate in dense vegetation.

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Section: B Related Workmentioning

confidence: 99%

Support Surface Estimation for Legged Robots

Homberger

Wellhausen

Fankhauser

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Komma et al performed Bayesian classification on six terrain types with accuracy of up to 95% [14]. Bayesian classification was also done by Kim et al who went beyond terrain classification by using classified terrains to estimate the friction coefficient of the ground from known terrain type friction coefficient values [15]. A hybrid approach to visual classification used by Brooks and Iagnemma trains a vibration classifier using an SVM classifier with handlabelled data, then these vibration classifications are used to train a visual feature based (colour and texture) SVM classifier in a self-supervised manner [17].…”

Section: Related Workmentioning

confidence: 99%

Terrain Roughness Identification for High-Speed UGVs

Wilson¹,

Ramírez-Serrano²

2014

JACR

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“…Besides vision, legged robots are often equipped with other sensors, so information from multiple sensors for terrain recognition is also available. Kim [12] used the friction coefficient of different terrains to classify terrains using the Bayes classifier. Ojeda [13] proposed a terrain classification method based on an integration of information from multiple sensors (gyroscopes, accelerometers, encoders).…”

Section: Introductionmentioning

confidence: 99%

SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

Zhu

Jia

et al. 2019

Applied Sciences

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In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.

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Non-contact terrain classification for autonomous mobile robot

Cited by 8 publications

References 12 publications

Support Surface Estimation for Legged Robots

Support Surface Estimation for Legged Robots

Terrain Roughness Identification for High-Speed UGVs

SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

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