## 5 Conclusions and Future Work  
In this work, we presented a set of methods for the classification of objects in indoor scenarios. Our solution extracts relevant information to identify the distinctive features of objects based on their physical structure. We consider different types of objects of interest for service robot that moves in a 2D  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object** & **Method** & **Error (cm)** & **Deviation (cm)** \\ \hline Chair (type 1) & Angle Segmentation (ours) & 4.53 & 1.37 \\ Chair (type 1) & FO [38] & 11.3 & 5.43 \\ Chair (type 1) & SS [39] & 6.35 & 2.15 \\ \hline Chair (type 2) & Angle Segmentation (ours) & 15.75 & 8.73 \\ Chair (type 2) & FO [38] & 81.76 & 70.85 \\ Chair (type 2) & SS [39] & 8.36 & 4.49 \\ \hline Door & Depth Segmentation (ours) & 8.87 & 3.27 \\ Door & FO [38] & 43.4 & 5.18 \\ Door & SS [39] & 47.35 & 8.92 \\ \hline People & Height Segmentation (ours) & 8.38 & 5.34 \\ People & FO [38] & 19 & 11.28 \\ People & SS [39] & 9.42 & 4.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average error distance between the actual position of the detected objects and their estimated positions in the map. People were static and 20 repetitions were run for each case.  
plane, also accounting for people around. Focusing on physical properties for segmentation, we can avoid the use of complex training phases as well as resource-hungry online deep learning algorithms and exploit low-density point-cloud information as provided by 3D LiDARs. In this way, we embrace a perception technology that is becoming increasingly popular, showing its benefits also for indoor scenarios. The high detection and positioning accuracy as well as the limited requirements in terms of processing resources show, indeed, that our approach is practical and can support semantic mapping efficiently also on low-end service robots.  
We are currently planning to exploit the presented results to enable the effective navigation and operation of service robots in indoor environments populated by persons, supporting the natural co-existence of robots and humans while preserving their privacy. We would like to explore the possibilities to improve the accuracy of our detection methods by integrating them with solutions based on 3D SLAM.  
Conceptualization, C.M.S., M.Z., and J.C.; software and validation, C.M.; writing and editing, C.M.S., M.Z. and J.C.; supervision, M.Z., J.C., and P.J.M.; funding acquisition, P.J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABT & Astra Body Tracker \\ ELD & Edge Leg Detector \\ CNN & Convolutional Neural Networks \\ FO & Find Object package \\ FoV & Field of View \\ GPU & Graphic Processing Unit \\ HRC & Height Range Cloud \\ PFF & Point-cloud Fast Filter \\ PFF\_PeD & Point-cloud Fast Filter for People Detection \\ SLAM & Simultaneous Localization And Mapping \\ SS & Semantic SLAM package \\ \end{tabular}