Blind and visually impaired people currently have inconveniences locating themselves in the indoor environment. No standardized system exists for them yet. After an inventory of the requirements of blind people, different representations do qualify for providing specific information blind people need. The main research question is: "How can blind people localise themselves (near) real-time in indoor environments with the combination of 3 representations of reality, namely (1) LiDAR point cloud matching, (2) ArcGIS Indoors and (3) Audio dynamic tactile map as the user interface?". Room detection and positioning of the user within the room are obtained by LiDAR scanning and point cloud matching. The processed point cloud height raster grids are acquired and imported into the Esri ArcGIS platform. The rooms are geo-referenced, and data is enriched by contextual awareness. As a user interface for blind people this report proposes two deliverables: a dynamic tactile map and an added or stand-alone audible supported user interface. Preliminary results of the qualitative validation show positive outcomes. This report is a stepping stone for the possibility of integrating multiple into one device.

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the concept of classifying point cloud by using deep learning, specifically in the indoor environment. This is done by first doing a neural network comparison and then doing a case study. In the neural network comparison, a look is taken into which of the neural networks that are capable of working with point clouds is best suited for our experiments in the indoor scene, based on the training speed, accuracy, ease of use concerning training on external datasets and setting up the network and space efficiency. After the comparison, we chose to continue with the PointCNN network during the case study. The case study is performed on data the NS (Nederlandse Spoorwegen) provided to us and all test results we got from our experiments can be visualized using the web application we developed along with this project. The purpose of the case study is to add extra value to the indoor LiDAR point cloud the NS has captured from Amersfoort Station by using deep learning to automatically classify assets present in their data. The value is in purposes, such as asset management, where the data does not need possibly hundreds of man-hours to be labelled. This saves a lot of time and also money each time a scan is made. In the case study we found through 4 different experiments that unbalanced data makes for bad results, but when a scene is labelled correctly very good results can be found in a local scene.