A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Table 2

Comparison of the proposed terrain classification framework with traditional method on benchmark feature vectors. MMFCC and FFT are adopted as benchmark feature vectors for acoustics and acceleration data sources, respectively. The driving speed is 2 m/s. The number in brackets is the dimensionality of the optimal feature subset corresponding to the best result.

Data source	Feature vector	SVM	RF	kNN	NB
	MMFCC (18D)	89.6%	87.1%	80.8%	77.9%
	FFT (200D)	90.4%	89.17%	75.42%	84.6%
	FFT (200D)	94.2%	92.92%	72.08%	88.3%
	FFT (200D)	92.1%	92.95%	91.3%	88.2%
	FFT (200D)	93.3%	92.92%	91.3%	90.8%
Proposed framework		97.9% (80D)	99.6% (90D)	95% (50D)	97.5% (100D)