## 4 Results and Discussion
### Detection Accuracy  
We also evaluated the detection accuracy of our method with the same experimental setup. For that, we propose as metric the _True Positive Rate_, which is calculated dividing the true positive detections (objects correctly detected) by the number of samples. Table 4 shows the results obtained with several approaches, using a number of 1000 samples (frames or point-clouds) for each case. The results are separated depending on the type of object to detect. It is also important to highlight that, in the case of approaches based on cameras, unlike it happens with LiDARs, illumination conditions may affect significantly the results. Therefore, we selected favorable illumination conditions to run the experiments and compare them fairly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Object** & **Method** & **Sensor** & **Samples** & **Horizontal FoV** & **Processing Time** & **Standard Deviation** \\ \hline Any & Ours & VLP-16 & 1000 & 360\({}^{\circ}\) & 21.22 ms & 3.28 ms \\ People & PFF\_PeD [13] & VLP-16 & 1000 & 360\({}^{\circ}\) & 27.97 ms & 3.53 ms \\ People & ELD [17] & VLP-16 & 1000 & 360\({}^{\circ}\) & 89.82 ms & 0.5 ms \\ Any & FO [38] & Astra Camera & 1000 & 60\({}^{\circ}\) & 29.65 ms & 3.72 ms \\ Any & SS [39] & Astra Camera & 1000 & 60\({}^{\circ}\) & 325.75 ms & 40.5 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average time employed by the different tested algorithms for processing the sensor readings and detecting objects.  
First, we analyze the detection of chairs. These are objects hard to detect, as they can present multiple shapes, sizes, and colors, depending on the model. Our Angle Segmentation method achieves a fairly good detection accuracy, only 6% below the best method, which was SS. However, as already discussed, worse lighting conditions can jeopardize SS results. Indeed, repeating our experiments with sunlight through the windows, detection accuracy for the SS method was reduced up to a 50%. In the case of the FO package, we had to add 50 images of each chair model in the environment, taken from different angles and distances. We tried first using only 10 images, but the accuracy was below 40%.  
In the case of doors, there are fewer works able to detect them, so we compared our Depth Segmentation method against the packages FO and SS. Our method outperformed clearly the alternatives, as the others found difficulties to detect closed doors, as in this case, relevant characteristics are harder to be distinguished, mistaking it with the wall, which had the same color too. Even with the doors open, another wall of the same color could be seen through the door, which confused the visual-based approaches.  
Finally, regarding the detection of people, our Height Segmentation method achieved a very high detection accuracy (96.95%), quite close to the best method, which was SS using CNNs (97.5%). We improved results from our previous work (PFF_PeD) when using both the LiDAR and the Astra camera. The ABT based on skeleton detection presented results slightly worse than our method, while the leg detection (ELD) performed considerably worse, presenting also a high amount of false-positives.