**3.1. Experiment 1: indoor MMSs data**

We selected a floor in our university as the indoor environment. We prepared a point cloud taken from an indoor MMSs (TIMMS, Nikon-Trimble), which consisted of a laser scanner, an omni-directional camera, inertial measurement units (IMU), and a wheel encoder, as shown in **Figure 9**. Acquired point-cloud data, point-cloud rendering results,

**Figure 9.** Indoor MMSs.

planes to be integrated into a larger plane or deleted as a small segment. When a specified plane is extracted, the direction of a normal vector and the cluster number are available as initial value inputs. The point-cloud clustering methodology for extracting the intersection of planes as ridge lines requires appropriate initial values such as curvature, fitting accuracy and distances to closed points [19]. However, our approach can extract boundaries from a point

We conducted three experiments using point-cloud data acquired in indoor and outdoor environments. Here we present the point-cloud clustering results of these experiments.

We selected a floor in our university as the indoor environment. We prepared a point cloud taken from an indoor MMSs (TIMMS, Nikon-Trimble), which consisted of a laser scanner, an omni-directional camera, inertial measurement units (IMU), and a wheel encoder, as shown in **Figure 9**. Acquired point-cloud data, point-cloud rendering results,

cloud without these parameters.

**Figure 8.** Normal vector-based point clustering.

84 Recent Applications in Data Clustering

**3.1. Experiment 1: indoor MMSs data**

**3. Experiments**

and point-cloud clustering results are shown in **Figure 10**. The results show that building features such as ceilings, beams, window shades, pillars, benches, and floors are classified clearly.
