Research Article
A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots
Table 1
Accuracies with individual data source and handcrafted feature vector at 2 m/s.
| | Data source | Feature vector | SVM | RF | kNN | NB |
| | Acoustics | MMFCC | 89.6% | 87.1% | 80.8% | 77.9% | | FFT | 82.9% | 70% | 56.7% | 55.4% | | Gianna and shape | 58.8% | 67.9% | 53.8% | 64.2% |
| | Wheel vibration | Temporal | 68.3% | 62.5% | 62.1% | 56.3% | | FFT | 90.4% | 89.2% | 75.4% | 84.6% | | Frequency characteristics | 59.6% | 64.2% | 62.5% | 51.7% |
| Centroid vibration
-Axes | Temporal | 41.7% | 52.9% | 51.3% | 47.5% | | FFT | 94.2% | 92.9% | 72.1% | 88.3% | | Frequency characteristics | 46.7% | 54.2% | 47.1% | 44.2% |
| Centroid vibration
-Axes | Temporal | 42.5% | 44.2% | 50% | 46.7% | | FFT | 92.1% | 93% | 91.3% | 88.2% | | Frequency characteristics | 44.6% | 44.6% | 46.7% | 45.1% |
| Centroid vibration
-Axes | Temporal | 44.2% | 46.3% | 50% | 45.5% | | FFT | 93.3% | 92.9% | 91.3% | 90.8% | | Frequency characteristics | 42.5% | 48.3% | 46.7% | 42.5% |
|
|
