# Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
## 5 Classification Tests With Real 3D Laser Scans  
Figures 9a-11a show a park, a rural path and an underpass where Andabata has been teleoperated. For each scene, a levelled 3D point cloud with a horizontal resolution of 1.2\({}^{\circ}\) and without intensity data has been obtained in 3.75 s [23]. Sky visibility determines the number of points for each laser scan, ranging from 32,795 for the park to 83,183 for the underpass.  
All this real data has been manually tagged to serve as ground truth (see Figures 9b-11b). In these figures, it is noticeable that the blind area on the ground is reduced because Andabata was moving during scan acquisition. The hand-labelled data contains a total of 162,999 points, where 90,074 belong to the traversable class.  
Feature extraction and traversability prediction for each scan can be obtained in 2.5 s for all the estimators with the exception of KNN that requires 2.7 s. In any case, these classification times make possible to process each 3D laser scan separately for autonomous navigation. Only 3215 points have not been classified due to the lack of five neighbours.  
Table 4 contains the confusion matrices obtained by each classifier. The balanced accuracy indices corresponding to this table can be found in Table 5. With real data, a slightly worst accuracy is achieved than with synthetic data, but it is still very high. The worst ranked estimators--GNB and LSVM--coincide with those pointed out in previous section. It also repeats as best ranked RF, this time accompanied by KNN.  
Figures 9c-11c illustrate the results of applying the RF classifier to the three real points clouds. Good classification results can be observed visually for all these scenes in these figures. Nevertheless, they contain errors such as some isolated green points on the slope near the rural path and on the vertical walls of the tunnel.  
Figure 9: A photograph of a park (**a**) and a real 3D point cloud from Andabata manually-tagged (**b**) and classified with RF (**c**).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Classifier** & **TP** & **TN** & **FP** & **FN** \\ \hline Decision Trees & 66,060 & 80,747 & 6918 & 6059 \\ Gaussian Naive Bayes & 51,110 & 83,541 & 4123 & 21,010 \\ K-Nearest Neighbors & 64,810 & 84,887 & 6679 & 3408 \\ Linear Support Vector Machine & 69,630 & 74,623 & 13,192 & 2339 \\ Bagged Decision Trees & 66,423 & 81,624 & 6027 & 5710 \\ Random Forest & 67,390 & 81,175 & 6475 & 4744 \\ Gradient Boosted Trees & 65,025 & 80,657 & 7893 & 6209 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Components of the confusion matrices for real data.  
Figure 10: A photograph of a rural path (**a**) and a real 3D point cloud from Andabata manually-tagged (**b**) and classified with RF (**c**).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Classifier** & **PR** & **RE** & **F1** & **BA** & **MC** \\ \hline Decision Trees & 0.905 & 0.916 & 0.911 & 0.919 & 0.836 \\ Gaussian Naive Bayes & 0.925 & 0.709 & 0.803 & 0.831 & 0.692 \\ K-Nearest Neighbors & 0.907 & 0.950 & 0.928 & 0.939 & 0.873 \\ Linear Support Vector Machine & 0.841 & 0.968 & 0.900 & 0.909 & 0.814 \\ Bagged Decision Trees & 0.917 & 0.921 & 0.920 & 0.926 & 0.852 \\ Random Forest & 0.912 & 0.934 & 0.923 & 0.930 & 0.859 \\ Gradient Boosted Trees & 0.892 & 0.913 & 0.902 & 0.912 & 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Balanced accuracy indices for real data.