# Smartphone-Based Indoor Localization within a 13th Century Historic Building

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}walkable area.

## 1. Introduction

^{2}building acts as a museum of the medieval town Rothenburg ob der Tauber [1], Germany.

- The pedestrian’s movement is modelled in a more realistic way using a navigation mesh, generated from the building’s floor plan. This only allows movements that are actually feasible, e.g., no walking through walls. Compared to the gridded graph structure we used before [8], the mesh allows continuous transitions and reduces the required storage space drastically.
- To enabled more smooth floor changes, a threshold-based activity recognition using barometer and accelerometer readings is added to the state evaluation process of the particle filter. The method is able to distinguish between standing, walking, walking up and walking down.
- To address the problem of sample impoverishment in a wider scope, we present a simplification of our previous method [3]. This reduces the overhead of adapting an existing system to the proposed method and allows to incorporate it directly to the state transition of any approach, using a general particle filter methodology.

## 2. Related Work

## 3. Recursive State Estimation

## 4. Transition

## 5. Evaluation

#### 5.1. Wi-Fi

#### 5.2. Activity Recognition

## 6. Particle Filtering

#### 6.1. State Estimation

#### 6.2. Sample Impoverishment

## 7. Experiments

^{2}in size. Due to objects like exhibits, cabinets or signs not all positions within the building were freely accessible. For the sake of simplicity we did not incorporate such knowledge into the floor plan. Thus, the floor plan consists only of walls, ceilings, doors, windows and stairs. It was created using our 3D map editor software (see Figure 4) based on architectural drawings from the 1980s. Our map editor is also used to automatically create the navigation mesh, which only takes a few seconds to compute.

- Acquiring a blueprint or architectural drawing of the building including at minimum the walls and stairs of the respective floors.
- Based on this 2D drawing, the floor plan is created manually using our 3D map editor (cf. Figure 4), comparable to software like Inkscape or FreeCAD.
- If necessary, create or improve the Wi-Fi infrastructure by plugging in beacons to available power sockets and compose a whitelist of MAC-addresses of the involved access points or beacons.
- Record the reference measurements based on the reference positions given in the floor plan.
- The Wi-Fi model is optimized using the previously obtained reference measurements.
- The navigation mesh is created automatically based on the before created floor plan as can be seen in Figure 1c.

#### 7.1. Transition

#### 7.2. Wi-Fi Optimization

#### 7.3. Localization Error

#### 7.4. Activity Recognition

#### 7.5. Estimation

## 8. Conclusions

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Floor plan (

**a**) and automatically generated transition data structures (

**b**,

**c**) for the ground floor of the historic building ( 71 $\mathrm{m}$ × 53 $\mathrm{m}$). To reach every nook and cranny, the generated graph (

**b**) requires many nodes and edges. The depicted version uses a coarse node-spacing of 90 $\mathrm{c}$$\mathrm{m}$ (1700 nodes) but barely reaches all doors and stairs. A navigation mesh generated for the same building required only 320 triangles (

**c**) and reaches every corner within the building.

**Figure 2.**Decision tree describing the threshold-based activity recognition using the smartphone’s barometer and accelerometer measurements. The respective thresholds are given by ${t}_{\mathrm{acc}}$ and ${t}_{\mathrm{baro}}$. For each sensor the sigma of the arithmetic mean $\Delta \overline{\omega}={\overline{\omega}}_{\mathrm{l}}-{\overline{\omega}}_{\mathrm{s}}$ of two different fix-sized windows ${\mathit{\omega}}_{\mathrm{s}}$ (short) and ${\mathit{\omega}}_{\mathrm{l}}$ (long), holding a set of the most current sensor measurements, is calculated. The process updates with every incoming barometer reading.

**Figure 3.**An example of the occurrence of sample impoverishment enhanced by a restrictive transition model that prevents sampling through walls. At time $t-1$ the approximated position (green line) drifts apart from the ground truth (black line) due to uncertain measurements. The posterior distribution is then captured within the room and not able to recover by itself [3].

**Figure 4.**The 3D map editor we developed to create the floor plans. This screenshot shows the ground level of the building. The window is split into toolbar (left), layers (upper right), parameters of current selection (lower right), drawing mode (upper center) and 3D view (lower center).

**Figure 5.**The two mobile applications developed for Android. The localization app in (

**a**) is used to record the Wi-Fi reference measurements based on the positions provided by the floor plan. In this screenshot the dialog for recording them is visible. The app also implements the here presented approach and can thus be used for localization. However, for the utilized experiments we used a simpler client (

**b**) allowing for user input like a ground truth or activity button.

**Figure 6.**Simple staircase scenario to compare the old graph-based model with the new navigation mesh. All units are given in meter. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are colored according to their z-coordinate. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.

**Figure 7.**Ground level of the building in the $xy$-plane from above. Includes the locations of the reference points, the ground truth and the optimized APs. The grey line connects an AP with the corresponding optimization. The colored borders are areas of special interest and are discussed within the text. The corresponding pictures on the right side show the museum in these places.

**Figure 8.**All conducted walks within the building. The arrows indicate the running direction and a cross marks the end. For a better overview we have divided the building into three floors, which are connected by four stairs (numbered 1–4). However, each floor consists of different high levels. They are separated from each other by different shades of grey, dark is lower than light.

**Figure 9.**Error development over time of a single particle filter run of walk 0. Between 10 $\mathrm{s}$ and 24 $\mathrm{s}$ the Wi-Fi signal was highly attenuated, causing the system to get stuck and producing high errors. Both, the simple and the ${D}_{\mathrm{KL}}$ anti-impoverishment method are able to recover early. However, between 65 $\mathrm{s}$ and 74 $\mathrm{s}$ the simple method produces high errors due to the high random factor involved.

**Figure 10.**(

**a**) A small section of walk 3. Optimizing the system with a global Wi-Fi optimization scheme (blue) causes a big jump and thus high errors. This happens due to highly attenuated Wi-Fi signals and inappropriate Wi-Fi parameters. We compare this to a system optimized for each floor individually (orange), resolving the situation a producing reasonable results; (

**b**) Error development over time for this section. The high error can be seen at 190 $\mathrm{s}$.

**Figure 11.**(

**a**) Occurring bimodal distribution caused by uncertain measurements in the first $13.4$ $\mathrm{s}$ of walk 1. After $20.8$ $\mathrm{s}$, the distribution gets unimodal. The weigted-average estimation (orange) provides a high error compared to the ground truth (solid black), while the KDE approach (blue) does not; (

**b**) Error development over time for the complete walk. From 230 $\mathrm{s}$ to 290 $\mathrm{s}$ to pedestrian was not moving.

**Figure 12.**Estimation results of walk 2 using the KDE method (blue) and the weighted-average (orange). While the latter provides a more smooth representation of the estimated locations, the former provides a better idea of the quality of the underlying processes. In order to keep a better overview, the top level of the last floor was hidden. The colored rectangles mark interesting areas. Within the green rectangle, the above mentioned differences between the two methods are clearly visible. The purple rectangle displays a situation in which a sample impoverishment was successfully resolved. The teal rectangle marks an area were both methods do not provide sufficient results.

**Table 1.**Overall localization results in meter using the different impoverishment methods. For estimation we used the KDE-based method, as the errors compared to the weighted-average differ by only a few centimeter. The results are presented given the average positioning error $\overline{x}$, the standard deviation $\overline{\sigma}$ and the 75%-quantil of positioning errors ${\tilde{x}}_{75}$.

None | Simple | ${\mathit{D}}_{\mathbf{KL}}$ | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

$\overline{\mathit{x}}$ | $\overline{\mathit{\sigma}}$ | ${\tilde{\mathit{x}}}_{\mathbf{75}}$ | $\overline{\mathit{x}}$ | $\overline{\mathit{\sigma}}$ | ${\tilde{\mathit{x}}}_{\mathbf{75}}$ | $\overline{\mathit{x}}$ | $\overline{\mathit{\sigma}}$ | ${\tilde{\mathit{x}}}_{\mathbf{75}}$ | |||

walk 0 | $13.4$$\mathrm{m}$ | $11.2$$\mathrm{m}$ | $22.6$$\mathrm{m}$ | $7.1$$\mathrm{m}$ | $6.6$$\mathrm{m}$ | $9.4$$\mathrm{m}$ | $5.8$$\mathrm{m}$ | $4.9$$\mathrm{m}$ | $7.3$$\mathrm{m}$ | ||

walk 1 | $3.2$$\mathrm{m}$ | $2.4$$\mathrm{m}$ | $4.1$$\mathrm{m}$ | $3.2$$\mathrm{m}$ | $2.6$$\mathrm{m}$ | $4.0$$\mathrm{m}$ | $3.8$$\mathrm{m}$ | $3.2$$\mathrm{m}$ | $4.6$$\mathrm{m}$ | ||

walk 2 | $8.3$$\mathrm{m}$ | $4.1$$\mathrm{m}$ | $10.9$$\mathrm{m}$ | $3.6$$\mathrm{m}$ | $2.3$$\mathrm{m}$ | $4.9$$\mathrm{m}$ | $3.6$$\mathrm{m}$ | $2.3$$\mathrm{m}$ | $4.8$$\mathrm{m}$ | ||

walk 3 | $7.0$$\mathrm{m}$ | $5.9$$\mathrm{m}$ | $13.5$$\mathrm{m}$ | $5.4$$\mathrm{m}$ | $4.7$$\mathrm{m}$ | $7.7$$\mathrm{m}$ | $4.8$$\mathrm{m}$ | $4.3$$\mathrm{m}$ | $6.5$$\mathrm{m}$ |

**Table 2.**The resulting detection rates provided by the activity recognition for all conducted walks. As the method suffers from a (time) lag, caused by the used moving average, we shifted the measured activity according to the average lag over all walks ($2.96$ $\mathrm{s}$). Some cells of the table are empty, because the respective walk did not require this activity.

Standing | Walking | Walking up | Walking down | Overall | |
---|---|---|---|---|---|

walk 0 | 65.6% | 80.9% | - | 84.8% | 78.4% |

walk 1 | 49.9% | 84.1% | - | - | 67.5% |

walk 2 | 57.4% | 83.5% | 83.5% | 82.1% | 71.7% |

walk 3 | 45.7% | 77.5% | 85.1% | 77.8% | 61.3% |

overall | 51.4% | 81.5% | 84.3% | 82.1% | 67.9% |

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**MDPI and ACS Style**

Fetzer, T.; Ebner, F.; Bullmann, M.; Deinzer, F.; Grzegorzek, M.
Smartphone-Based Indoor Localization within a 13th Century Historic Building. *Sensors* **2018**, *18*, 4095.
https://doi.org/10.3390/s18124095

**AMA Style**

Fetzer T, Ebner F, Bullmann M, Deinzer F, Grzegorzek M.
Smartphone-Based Indoor Localization within a 13th Century Historic Building. *Sensors*. 2018; 18(12):4095.
https://doi.org/10.3390/s18124095

**Chicago/Turabian Style**

Fetzer, Toni, Frank Ebner, Markus Bullmann, Frank Deinzer, and Marcin Grzegorzek.
2018. "Smartphone-Based Indoor Localization within a 13th Century Historic Building" *Sensors* 18, no. 12: 4095.
https://doi.org/10.3390/s18124095