Terrain classification

2003 ◽  
Author(s):  
Alok Sarwal ◽  
David Simon ◽  
Venkat Rajagopalan
Keyword(s):  
Terrain Classification

Vibration-based Terrain Classification for Mobile Robots Using Support Vector Machine

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 660 ◽  
Author(s):  
Qiang LI ◽  
Kai XUE ◽  
He XU ◽  
Wenlin PAN ◽  
Tianlong WANG
Keyword(s):  
Support Vector Machine ◽  
Mobile Robots ◽  
Support Vector ◽  
Terrain Classification

scholarly journals Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

2021 ◽  
Author(s):  
Yue Zhao ◽  
Feng Gao ◽  
Qiao Sun ◽  
Yunpeng Yin
Keyword(s):  
Legged Robots ◽  
Measurement Unit ◽  
Support Vector ◽  
Hexapod Robot ◽  
Terrain Classification ◽  
Adaptive Locomotion ◽  
Joint Torques ◽  
Active Compliance ◽  
Outdoor Environments ◽  
The Stability

AbstractLegged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

A test of airborne, side-looking synthetic-aperture radar in central Newfoundland for geological reconnaissance

1991 ◽  
Vol 28 (2) ◽  
pp. 257-265 ◽  
Author(s):  
D. F. Graham ◽  
D. R. Grant
Keyword(s):  
Surface Roughness ◽  
Synthetic Aperture Radar ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Structural Elements ◽  
Terrain Classification ◽  
Regional Trends ◽  
Valuable Complement ◽  
Wide Swath ◽  
Aperture Radar

Side-looking, C-band synthetic-aperture radar (SAR) penetrates cloud and fog, and operates day or night, to produce pseudo-three-dimensional terrain images with enhanced topography and surface roughness. The images, which have a 20 m resolution and cover large areas, have been used to map the regional trends, patterns of lineaments, and terrain types over a 6200 km2 area of complex lithology, structure, and drift cover. Four lineament classes are differentiated. Glacial trends are clear, and bedrock structures (faults, fractures, joints, foliation, and folded bedding) with relief expression at the surface show through the drift as lineaments. They accurately reproduce most known features when compared with bedrock and Quatenary geology maps. Hitherto unrecognized structural elements are revealed. Tones and textures reflect minute surface roughness variations useful in terrain classification. SAR wide-swath-mode imagery is thus a valuable complement to aerial photography, and is superior in revealing hummocky moraine, ribbed moraine, boulder fields and stony till. Wider use of this imagery is encouraged.

scholarly journals Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

2021 ◽  
Vol 45 (6) ◽  
pp. 843-857
Author(s):  
Russell Buchanan ◽  
Jakub Bednarek ◽  
Marco Camurri ◽  
Michał R. Nowicki ◽  
Krzysztof Walas ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Estimation Error ◽  
Extreme Environments ◽  
Legged Robot ◽  
Terrain Classification ◽  
Localization Algorithm ◽  
Geometric Information ◽  
Contact Points ◽  
Monte Carlo Localization ◽  
Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

scholarly journals Terrain classification of ladar data over Haitian urban environments using a lower envelope follower and adaptive gradient operator

2010 ◽  
Author(s):  
Amy L. Neuenschwander ◽  
Melba M. Crawford ◽  
Lori A. Magruder ◽  
Christopher A. Weed ◽  
Richard Cannata ◽  
...  
Keyword(s):  
Urban Environments ◽  
Lower Envelope ◽  
Terrain Classification ◽  
Gradient Operator

Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment

2021 ◽  
Author(s):  
Akhil Kurup ◽  
Sam Kysar ◽  
Jeremy Bos ◽  
Paramsothy Jayakumar ◽  
William Smith
Keyword(s):  
Terrain Classification
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