Next Article in Journal
Security Aspects for Rpl-Based Protocols: A Systematic Review in IoT
Next Article in Special Issue
Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
Previous Article in Journal
Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces
Previous Article in Special Issue
Recent Developments Regarding Painting Robots for Research in Automatic Painting, Artificial Creativity, and Machine Learning
Open AccessArticle

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

1
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
2
Shenzhen Academy of Aerospace Technology, Shenzhen 518000, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(18), 6473; https://doi.org/10.3390/app10186473
Received: 21 August 2020 / Revised: 7 September 2020 / Accepted: 11 September 2020 / Published: 17 September 2020
(This article belongs to the Collection Advances in Automation and Robotics)
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. View Full-Text
Keywords: visual estimation; physical properties; two-stage; field terrain; dataset visual estimation; physical properties; two-stage; field terrain; dataset
Show Figures

Figure 1

MDPI and ACS Style

Dong, Y.; Guo, W.; Zha, F.; Liu, Y.; Chen, C.; Sun, L. A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain. Appl. Sci. 2020, 10, 6473.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop