Visual terrain classification for mobile robot using bag of words / 医疗卫生装备

ABSTRACT

Objective To design a visual terrain classification algorithm to facilitate the robot to make appropriate movement strategy by perceiving the surrounding environment.Methods Bag of words (BOW) and support vector machine (SVM) were used to develop a simple and effective terrain classification algorithm.The BOW model involved in feature extraction,codebook generation and feature coding.The mid-level feature developed by BOW model was then fed into SVM classifier to obtain the terrain classification result.Results The quadruped robot platform was applied to performing visual terrain classification experiment in the natural environment.The test environment included floor,asphalt,sand and grass.Good experimental results were achieved,and the classification accuracy was above 90％ (the beat was 97.54％ for grass).Conclusion The algorithm can effectively and accurately distinguish all kinds of terrains,with high accuracy and good stability.The key frame selection method needs researching in the future.