Many MLS point cloud application scenarios, such as navigation and localization algorithms, require only static environments, but the original MLS data usually inevitably includes many dynamic objects such as moving vehicles, bicycles, and pedestrians. Therefore, these dynamic objects need to be removed before using MLS point clouds. This thesis designs an efficient and memory-friendly Octomap-based dynamic object detection and removal method for MLS data. Firstly, the original MLS data is split into multiple data frames based on the timestamp of each capture point. Each data frame is inserted into a separate Octomap along with its neighboring data frames. The free points in all Octomaps are extracted by setting an occupancy probability threshold. Second, the region of interest (ROI) related to the dynamic object is delineated by the MLS sensor mounting height and the local large vehicle height limit. Only the free points located within the ROI are retained. Then the free-point rate and the multi-return rate are calculated for each free point using a fixed radius spatial search to denoise and detect vegetation points. Finally, the KNN spatial search is used to remove vegetation points and extract dynamic objects from the free points. The proposed method is tested in four case sites in Delft, the Netherlands and its producer’s and user’s weighted average dynamic object detection and extraction accuracies are 88.004% and 82.624%, respectively. The weighted average overall accuracy is 99.833%. Compared with the original Octomap, the proposed method is 35.472% more efficient on average and can be further accelerated by parallel computing, with a maximum memory consumption of only 42.437% of the original Octomap. The implementation results and accuracy assessment demonstrate that the proposed method can be effectively applied to dynamic object detection and extraction tasks in MLS data sets in a compute-friendly and memory-friendly way.

As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.