# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 4 Evaluation
### Testing Scenarios  
We used Microsoft HoloLens to record point clouds in three different locations in the building. The recorded point clouds cover a number of possible scenarios. Figure 5 shows the three scenarios including the total recorded area in square meters and the walked path. For every scenario, we recorded 20 frames of point-cloud data. The frames were recorded evenly spaced (about 1 m apart) along the walked path. To determine the ground truth, the recorded point-cloud frames have been aligned manually with a ground truth pose \(\hat{T}\). The following scenarios have been recorded:  
**Scenario S1:** The device starts in one room, moves out into the hallway, enters another room, turns back into the hallway, and enters a third room (see Figure 5a). The rooms in this scenario are distinct from other rooms in the building model. This scenario tests the accuracy when moving through multiple different rooms. The walked path has a total length of 21.02 m, and the point cloud frames cover a total area of 169.41 m\({}^{2}\).  
**Scenario S2:** The device starts in a single room, exits into the hallway, and moves around a corner (see Figure 5b). Many rooms in the given building model are self similar and can be confused for other rooms. The walked path is 18.65 m in length with 62.45 m\({}^{2}\) of area covered.  
**Scenario S3:** The device moves down a single hallway up until an intersection (see Figure 5c). Straight hallways are often difficult for localization algorithms, as they can be visually indistinguishable at different positions. The path has a length of 24.29 m, and the hallway has an area of 57.94 m\({}^{2}\).  
Figure 4: The 3D BIM model used for testing.  
The grid size of the rasterization has been set to 10 cm. The parameters for thresholding have been set to \(t_{f}=1\) and \(t_{w}=20\), which depend on the chosen spatial resolution of the HoloLens. To optimize the results, we used a grid search over the recorded data to determine the factors \(\alpha=0.6\) and \(\beta=0.65\).  
Each point cloud frame is fed into the algorithm, resulting in an estimated pose \(T\). We then calculate the positional and rotational difference \(\Delta t\) and \(\Delta\phi\) as a way of measuring localization accuracy. We categorize a localization as correct if \(\Delta t\) is smaller than 0.5 m and \(\Delta\phi\) is smaller than 5\({}^{\circ}\). Figure 6 shows the localization error of correctly localized frames. The average error over all correctly localized point clouds is about 10 cm-20 cm over the \(xy\) plane and 1 cm and 5 cm for the ground estimation. Localization errors for incorrect localizations are between 10 m and 100 m. No localization errors between 1 m and 10 m were observed. This gap can be explained as the algorithm mistaking the observed area as a room in a different part of the building. Since rooms are about 10 m in size, localization errors in between are unlikely.  
The reliability is measured through the point of first correct localization. At this point, the localization error is smaller than 0.5 m and 5\({}^{\circ}\). Table 1 shows the time, distance, and covered area of the first successful localization of each scenario. Notable is that, once the localization is correct, the localization stays correct for the remainder of the scenario.  
Figure 5: The three scenarios used for testing localization: Observed geometry is marked in yellow, and the path followed the red line. Red dots show selected points of recording used for testing.  
Figure 6: Localization error for position and rotation: The gray lines indicate the point in time when the first correct localization was measured. Localization results above 0.5 m and 5\({}^{\circ}\) have been omitted from the graph.