In our day to day activity, imagine if you go to a museum, hospital or any kind of huge building. You need to find the best way to get into a specific depart- ment. It might be difficult to find the way even if you have the map of the building in your hand. Indoor positioning systems can be used to locate people or objects inside a building, using radio waves, signals, or other sensory information collected by a smartphone or tablet. Bluetooth Low Energy (BLE) beacons broadcast signals, and bluetooth devices, such as smartphones, can then receive these signals. BLE devices can take Received Signal Strength Indication (RSSI) information together with an algorithm to calculate the location of the user. This is a useful method for indoor environments when using Global Positioning System (GPS) is not an option [1]. In this project I tried to find a better solution for localization and navigation when GPS does not work. The focus of the project is to use communication be- tween smartphones and beacons, for guidance in inside environments, without using GPS. This thesis is about the applications I produced, which can be used for indoor localization and navigation. Using the applications, you can map any building such as university, hospital, museum, big mall etc. To map a building, you upload a map of the building and put waypoints where you placed beacons. Once mapping is done, you can log in to the web admin and put some informa- tion for each beacon. As a user, when outside a nearby mapped building, your phone can get notified (trough bluetooth), and you can download the user app, which includes the map of the building and shows your location. With the user app you can easily find your favorite places in the building and get information about place near you.

La navigation inertielle grâce aux capteurs intégrés dans les smartphones permet d’assurer une géolocalisation continue même en absence de signal GNSS. Ces capteurs bas coût délivrent néanmoins des mesures bruitées qui engendrent une dérive de la trajectoire. La technique PDR qui est une technique de navigation inertielle par détection de pas souffre de deux limites principales. La première est l’estimation de la longueur de pas car cette dernière dépend des caractéristiques physiques de chaque utilisateur, et la seconde est le résultat d’une dérive angulaire combinée avec un biais lié au portage du capteur à la main. Dans le contexte du projet HAPPYHAND, ce travail s’intéresse à l’exploitation de la carte pour corriger ces différentes erreurs. Un réseau de navigation topologique est exploité pour corriger à la fois les erreurs angulaires et calibrer le modèle de longueur de pas. Ce modèle est ensuite augmenté par un processus de mise à jour de position par détection de points d’intérêt. / Smartphone navigation using the low-cost embedded sensors in off the shelf smartphones can provide a continuous solution in GNSS-denied environments. The most widely adopted approach is Pedestrian Dead Reckoning (PDR) that uses acceleration and angular velocity to estimate the user’s position. Yet, consumer grade sensors deliver noisy measurements that may result into a drift in the estimated trajectory. One major challenge is to estimate accurately step length information since it depends on physiological features that are specific to each user. In addition, angular biases are more likely to be introduced in the orientation estimation process with handheld devices. This is mainly due to the high degree of freedom of hand motion. In the context of a national project called HAPPYHAND, the main goal of this work is to exploit map information as far as possible in order to mitigate the previous inherent limitations to the PDR approach. First, a topological network extracted from the map is proposed in order to correct the angular errors and calibrate the step length model. Second, context awareness is adopted in order to provide regular and frequent position updates thanks to a point of interest online detection scheme.

This thesis is focused on creation of building information model (BIM) for existing buildings. The main objective of this work is to develop the methodology (workflow) for the creation of BIM model using selected geodetic methods, specifically for the modeling based on the existing documentation of the building, the modeling from the handheld distance meter and the modeling from point cloud acquired by the indoor mapping system. The aim of these workflows is to explore the suitability of these methods, to check the limits of each method and to point out the potential issues. Revit (version 2013 and 2014) was used as an authoring environment for creation of the models. Workflow for modeling based on the documentation of the building shows how to insert drawings into Revit and how to create a model based on these drawings. The workflow was developed based on experience with creation of model of an office building. The workflow for handheld laser distance meter describes how to work with rangefinder equipped with Bluetooth, which allows user to create a model onsite. The third part of this thesis deals with creation of BIM from pointcloud acquired by indoor mobile mapping system. The workflow describes data collection and point clouds processing directly in Revit using the ScanToBIM extension. The results of this work are methodical instructions for the methods described above and the comparison of these methods. Workflows contain recommended procedures and highlight common issues and mistakes. This should enable the readers of this thesis to choose the right method and avoid common mistakes.