## 4 Results and Discussion
### Positioning Precision  
In this section, we show results to assess the accuracy of our method in terms of position estimation of the detected objects in the map, in relation to their real positions in the scenario. Table 5 depicts the results of our different methods with FO and SS as alternatives. The precision error is computed comparing the estimated position with the actual geometrical center of each object, that we measured by hand as ground truth. In the case of the chairs. two different models were considered with a different number of legs. Chairs of type 1 are like the first, second, and last chairs from left to right in Figure 6(a), whereas chairs of type 2 were like the remaining model with 4 legs. For the chairs of type 1, our Angle Segmentation method got an error of less than 5 cm, as the estimated position depends on the width of the leg of the chair. For the chairs of type 2, our solution had an error of almost 16 cm, as the estimation in this model depends on the number of legs that are detected to estimate the position of the geometric center. For instance, if only one leg is detected the error is greater than when detecting all 4. As for the results obtained with the FO package, two aspects can be observed. For chairs of type 1 the error is larger than with our method. FO makes detections using the color image and once the detection is made, the 3D position is estimated from the depth information of the camera. Part of the error comes from using pixels of the back of the chair to estimate the depth, instead of the center. For chairs of type 2, the error is considerably higher. In both cases, it is important to note that FO \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Object** & **Method** & **Sensor** & **True Positive Rate** \\ \hline Chairs & Angle Segmentation (ours) & VLP-16 & 67.3\% \\ Chairs & FO [38] & Astra Camera & 63.7\% \\ Chairs & SS [39] & Astra Camera & 73.2\% \\ \hline Doors & Depth Segmentation (ours) & VLP-16 & 85.4\% \\ Doors & FO [38] & Astra Camera & <20\% \\ Doors & SS [39] & Astra Camera & 37.3\% \\ \hline People & Height Segmentation (ours) & VLP-16 & 96.95\% \\ People & SS [39] & Astra Camera & 97.51\% \\ People & PFF\_PeD [13] & VLP-16 & 90.1\% \\ People & PFF\_PeD [13] & Astra Camera & 95.8\% \\ People & ABT [23] & Astra Camera & 95\% \\ People & ELD [17] & VLP-16 & 63.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy in object detection. Depending on the type of object we compare different approaches. makes use of CNNs that are trained with rectangular images to detect objects on the images, and then, a 3D position is extracted from the depth information of that noisy detection. Using 3D information from point-clouds directly to train the CNNs would improve results. Moreover, in the case of SS, this algorithm created a 3D map with the information sensed using OctoMap with a resolution of 5 cm for each voxel. In part due to this, errors were in that order, 6.35 cm for chairs of type 1 and 8.36 cm for chairs of type 2. For the door detection, our Depth Segmentation method reports an error significantly lower than the others. In part, this is because, on many occasions, FO and SS are estimated as the position of the door a point in the left or right side of the door frame, instead of the center. Instead, using LiDAR information, the middle point can be estimated with higher precision. Regarding people pose estimation, our proposal for Height Segmentation achieved the best performance. It is important to note that this test was carried out with static people for comparison. Our solution presents a better estimation of the position than methods that use cameras. Besides, these methods based on cameras had a limited range of up to 5 m to detect people, while our method could detect people up to 12 m thanks to the VLP-16 LiDAR. Figure 10 presents the final results for our semantic map method in an indoor office scenario. As already explained, we teleoperated our service robot as the semantic map was built.