**4. Discussion**

This paper describes a terrain classification system for a multilegged robot on complex terrain with obstacles. Several topographic classification methods are summarized in Table 5 [8,11,17,37–41]. With respect to single terrain recognition, several successful single terrain classification methods proved the effectiveness of this kind of methodology via local features, BoW model, and SVM. The common points (also advantages) of these works and our proposed algorithm include the following: Image features are extracted by selecting local features. Unlike color-based and spectra-based methods, local features are invariant to scale, rotation, brightness, and contrast and hence have become popular in image classification. In these methods, the SURF algorithm is used to extract local features of terrain images as input to the BoW model. The performance of SVM in classifying a small number of samples is also excellent. The characteristics of sensor-based information including frequency of leg current [38] and tactile data [40] are also used for terrain recognition. This kind of data is similar in the same terrain and has certain regularity in different terrains. The methods of building the classifier mainly focus on SVM [8,11,17], neural network [37–39], and mixtures of Gaussians [40,41]. Among them, SVM and neural network are the two main classification models. The application of terrain identification to legged robots is mainly concentrated on gait transition and path planning. For terrain classification with a multilegged robot, the precision requirement is low and a simple SVM is good enough for expected results. Our image infilling algorithm has the effect of magnifying local features of the image, which makes the classification more accurate. We also made an innovation in feature extraction: the SURF-BRISK algorithm is more suitable for real-time classification, its matching speed is much faster than the SURF algorithm alone, and its accuracy is also in line with the SURF algorithm.


**Table 5.** Comparison of recent terrain classification methods.
