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Xiong Y, Ge Y, Grimstad L, From PJ. An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation. J FIELD ROBOT 2019. [DOI: 10.1002/rob.21889] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Ya Xiong
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅs Norway
| | - Yuanyue Ge
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅs Norway
| | - Lars Grimstad
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅs Norway
| | - Pål J. From
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅs Norway
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SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9091779] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
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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