## 1 Introduction
The LIDAR scanners most often used for localization are categorized into two types based on the shape formed by the transmitted beams: two-dimensional (2D or planar), where the light beams are confined to a single plane, and three-dimensional (3D or spatial), where the beams are distributed across three dimensions. Long range, field of view, and precision have made spatial LIDAR scanners applicable in a variety of fields, including urban and underground exploration, archaeology, forestry, agriculture, environmental monitoring, and more [26]. This is supported by a growing body of research on data processing methods and an increasing number of sensor models from numerous manufacturers [27]. Additionally, 3D LIDAR scanners play an important role in autonomous driving [28; 29]. It is, however, typical that 3D point clouds are used to build a full 3D map of the whole area, therefore hugely increasing the computation time and memory utilization. Examples of this approach for localization in underground mines can be found in [30], and for outdoor and indoor scenarios in [31; 32]. Yet the localization time may greatly vary--in [32], the median time for the localization algorithm processing is about 1 s; in [31], the authors report it to be about 0.1 s. The latter may be sufficient in many applications, but it may also introduce unnecessary constraints to the robot performance. It is a natural expectation that reducing the amount of data to be processed, particularly in the complex stages of algorithms, will lead to a decrease in the processing time. However, it remains crucial that the retained data are sufficient to achieve acceptable precision. In this paper, we investigate the applicability of point density in detecting the idler frames of belt conveyors. Filtering regions with an increased density of point cloud points is expected to indicate the regions where the searched objects may be situated. The initial selection of these regions significantly reduces the number of points processed further in the detection and classification stages. Some researchers used density-based filtering to detect long objects, both natural such as trees [33; 34; 35] and artificial such as street poles [36] or bridge elements [37]. However, in all cases, the authors were processing dense point clouds obtained from geodesic 3D LIDAR, often using point clouds aggregated from multiple locations. Such an approach is not suitable for mobile robots, as in [34] the authors state that a single scan contains 30 mln points and its collection lasts 8 min, and in [33] it takes 2.5 min to collect 50 mln points, with 3 h of a single-scan processing. Another common element of those cases is that the authors search for objects that extend in one direction. After the first clustering based on density analysis, the selected regions are sliced with a single or multiple planes perpendicular to the direction in which the object extends and further analysis is performed for 2D images. The idler frames that are to be detected by the inspection robot are different from the long objects. They occupy a rather concise space and are placed regularly along the belt conveyor. Consequently, the mentioned methods dedicated to long objects cannot be applied directly. Motivated by that fact, this paper extends the previous approach by proposing a density analysis not in a single but in two orthogonal directions to allow the application of the method to concise objects. Additionally, the whole processing uses 3D points, not being restricted to slice planes. The contribution of the paper is a novel processing workflow of the 3D LIDAR scans for the detection of the idlers' supports. The main properties of the proposed algorithm are as follows:  
* The method utilizes selection of the objects based on density segmentation, but the processing scheme proposed in the paper differs from the previously mentioned works in that it is tailored with consideration of the requirements of mobile robots.