## 3 Semantic Mapping via Segmentation
### Height Segmentation  
The first segmentation approach that we investigate focuses on the detection of objects that have peculiar characteristics at different heights. Low-density point-clouds typically provide information in layers, as depicted in Figure 3. For mechanical 3D LiDARs, the layers correspond to the array of rotating lasers, each tilted with a different angle. For this reason, the obstacles that are at a further distance from the sensor have bigger gaps between layers. Figure 3 shows how the number and position of useful points to detect a person drastically change with distance. To implement such detection, it is necessary to define the various height ranges where the object of interest has distinctive features. If one or more perceived layers fall in such ranges and the measurements correspond to the physical structure of the target object, a match is found. This type of detection matches well with the perception of the human body, for instance. As represented in Figure 4, it is possible to identify two sectors of relevance: a height range with legs and one with the trunk of the human body. More specifically, denoting the height as \(h\), we distinguish between a _Lover Section_ (LS), defined as \(S_{1}<h<S_{1}+LS_{h}\), and a _Higher Section_ (HS), as \(S_{1}+LS_{h}+S_{2}<h<S_{1}+LS_{h}+S_{2}+HS_{h}\). Nonetheless, our method is more general, and we could define more sectors with different height ranges to detect other objects with different physical characteristics. This type of segmentation is a variation of the one proposed in our previous work [13] for the detection of moving people. The complete procedure is exemplified in Figure 5. Once the height ranges have been defined, all the points belonging to the different ranges are extracted and stored in different _Height Range Clouds_ (HRC), and the remaining points are discarded. To obtain these clouds it is also necessary to define how many points should each have, according to the horizontal angle  
Figure 3: Sensed human body at different distances from a 3D LiDAR (Velodyne VLP-16) located at a height of 40 cm above the floor. The person is positioned (**a**) 2 m, (**b**) 4 m, (**c**) 6 m, (**d**) 8 m, and (**e**) 12 m from the sensor. Figure 2: The architecture of our proposed approach for semantic mapping, based on multiple segmentation methods applied to detect different types of objects. resolution (\(H_{r}\)) of the sensor. Taking into account that multiple layers could fall in the same range, among vertically aligned points, the nearest one is selected. This is done for the complete FoV at the given \(H_{r}\) resolution. At the end of this step, one layer is constructed for each HRC with \(FoV/H_{r}\) points, as shown in Figure 5c. Within the layer of each HRC, clusters of points with a maximal Euclidean distance \(D_{max}\) between them are identified. Points in the point-cloud are visited in increasing angle, and the Euclidean distance (on the XY plane) between two consecutive points is measured. If greater than \(D_{max}\), a new cluster is created and the previous one is completed. Once this process is finished, the width of each cluster is compared to a set of reference widths for the parts of the object to be detected according to the different sections. In this particular case, a lower section minimum width (\(LS_{min}\)), a lower section maximum width (\(LS_{max}\)), a higher section minimum width (\(HS_{min}\)), and a higher section maximum width (\(HS_{max}\)) are defined. If the width of a cluster is outside these ranges, all its points are deleted from the HRC. For the remaining clusters, only the information about its centroid is preserved, as shown in Figure 5d. Finally, the centroids from clusters in the two different HRC are compared with each other, looking for a match. In the case of people detection, only the single points (i.e., a trunk) in \(HS\) with a corresponding pair of points (i.e., legs) in \(LS\) are preserved. These points are then labeled as the possible position of a detected human person.