## 1 Introduction  
Service robots are becoming common in our everyday life in an increasing number of scenarios. They can perform tasks autonomously and cooperate with humans to guide people in airports [1], assist the elderly [2], help in the education of children [3], aid staff and patients in hospitals [4], and be of service in work environments in general [5]. An essential skill in these environments is the ability to perceive the surroundings and the aspects of relevance for operations in social contexts. To achieve this, it is not only necessary to accurately detect people but also to identify those objects essential to enable the coexistence and the interaction between robots and humans. In indoor environments, recognizing objects like doors, chairs, and stairs (among others), can allow robots to understand better their environment and operate more efficiently with humans. For instance, detecting whether a room is closed or not, or whether a person is sitting in a room, is useful information that robots could use to react and adjust their ongoing plans accordingly. Moreover, if robots are to navigate in dynamic environments, object detection needs to be performed online in an efficient manner, so that the robots can react promptly to the presence of people or objects of interest. Ideally, we also want to avoid the need for extensive pre-training tasks. Nowadays, object recognition is performed mostly through the use of high-resolution visual cameras. This brings up issues related to human privacy, especially in public spaces, as service robots could perform face recognition or store/share recorded videos. However, alternative sensors like 3D LiDARs are becoming more and more common, extending the range of options for perception. Even though they are not still so widely used in indoor environments, their characteristics like Field of View (FoV) and detection distance, make them a valuable option to perceive objects efficiently and reduce privacy concerns, as confirmed by recent market trends [6; 7]. Table 1 compares the main characteristics of some typical sensors for perception in service robots, which we use in our experimentation. As we will discuss in Section 2, there exist multiple methods for adding, on top of the geometric map, a semantic layer including detected objects of relevance for robot navigation. We take navigation of service robots as the reference task, as any other task is likely to be based on navigation. In particular, we consider navigation in a 2D plane, according to the nature of service robots operating indoors. In this context, there are 2D and 3D approaches using different types of sensors to build geometric maps. From these maps, classification algorithms can recognize specific objects and add a new layer with semantic information on top of the physical one, the so-called semantic maps. Semantic maps can then be used to refine robot behaviors, computing more efficient plans depending on their surrounding information. At the best of our knowledge, however, existing solutions for building semantic maps either rely on rich information, as provided by visual cameras and/or high-end processing resources, to apply complex classification algorithms. In our previous work [13], we identified a set of effective techniques to process point-cloud data for the detection of people in motion. In particular, our goal was to exploit a set of specialized filters to process both high- as well as low-density depth information with constrained computing resources. This work extends our previous approach (Section 3) to address also static people as well as different objects of relevance (like doors and chairs) for navigation tasks in social contexts. In particular, we introduce (a) height, (b) angle, and (c) depth segmentation as a way to differentiate among objects depending on their physical properties. By analyzing the information along these directions, our approach offers a set of tools that can be combined together in different ways to specify the unique features of the objects to be detected.