Abstract
This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the frequency variations of the textured surface are analyzed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility-a hard surface, a soft surface, and an unwalkable area-our proposed method outperforms existing methods by up to 16%, and also provides improved robustness.
Highlights
H UMANOID robots have been developed in recent decades to replicate human movement, and cameras are frequently employed as primary sensors, to emulate the way our eyes perceive the navigable environment
We present a textured-based terrain classification method for a legged system using a single camera that offers the following novel contributions: i) a recursive temporal filter with adaptive filter coefficients computed from major uncertainties; ii) a compensation for the perspective foreshortening; iii) a new path consistency estimation; iv) a technique for performance improvement in terms of classification accuracy and computational cost using the motion characteristics of a biped humanoid robot
The radial basis function (RBF) kernel was employed in the support vector machine (SVM) classification
Summary
H UMANOID robots have been developed in recent decades to replicate human movement, and cameras are frequently employed as primary sensors, to emulate the way our eyes perceive the navigable environment. We present a textured-based terrain classification method for a legged system using a single camera that offers the following novel contributions: i) a recursive temporal filter with adaptive filter coefficients computed from major uncertainties; ii) a compensation for the perspective foreshortening; iii) a new path consistency estimation; iv) a technique for performance improvement in terms of classification accuracy and computational cost using the motion characteristics of a biped humanoid robot. We compute the decaying weights based on the major possible uncertainties due to walking and camera settings, which are motion blur, path consistency and frequency variation caused by perspective view These factors cause a change in texture characteristics and the information from the affected areas is weighted .
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