Figure 1
Automatic graphics output from experiments with RTL (a) Visibility diagrams [15]. (b) Correspondences [15]. (c) Pose uncertainty [16].
Figure 2
Segmentation of 2D and 3D point clouds. Different colors distinguish separate continuous clusters, black is reserved for outliers and helper drawings (a) Empty room scan with noise [15]. (b) Helix point cloud with step changes in radius.
Figure 3
Line segment and polygon extraction as used in [17]. (a) Extracted line segments. (b) Extracted polygons.
Figure 4
Speed benchmark of vectorization algorithms – semicircular point cloud [15]. (FTLS [11] – green, INC [18] – dark green, DP [19] – black, RW [20] – gray).
Table 1
Type traits of the Robotic template library, when applied on selected template objects. Type properties are examined in the left part of the table, while the applicability of geometrical transformations is summarized to the right. The traits are named in a positive manner, so if e.g. an object Obj has a metric defined, rtl::has_metric<Obj>::value is 
, otherwise it would be 
.