Accurate alignment of heterogeneous LiDAR point clouds is important for producing high-quality elevation data. This process, known as harmonisation, corrects spatial discrepancies between overlapping datasets collected at different times or using different sensors. A key step in harmonisation is the use of reliable benchmarks, which are features that are clearly detectable in both datasets, to guide the alignment. While artificial targets have been traditionally used as such benchmarks, their deployment is costly and impractical for large-scale or uncoordinated surveys. Consequently, there is growing interest in extracting natural or man-made features directly from LiDAR data to serve as benchmarks.

Road markings have recently been proposed as an alternative benchmark due to their consistent visibility in LiDAR point clouds, particularly through intensity value. As part of the Integrale Hoogtevoorziening Nederland (IHN) initiative, road markings are being explored as benchmarks for national point cloud alignment. However, the performance of the road markings as a co-registration benchmark has not been well researched yet.

This research investigates how the accuracy of automatically extracted road markings affects the quality of point cloud alignment. It builds upon an adaptive extraction method that adjusts intensity thresholds to suit different datasets and applies geometric filtering to generate 3D line representations of road markings. These extracted features are used to align heterogeneous LiDAR point clouds, and their performance is evaluated against manually digitised road markings to assess how extraction quality influences alignment accuracy. To support this evaluation, a RANSAC-weighted centroid alignment approach is proposed, which uses the inlier count of each correspondence as a weight during transformation estimation, aiming to prioritise geometrically stable benchmarks in the alignment process.

The results show that alignment accuracy is influenced by the extraction quality and distribution of road markings. When fewer road markings are available, especially in datasets with larger time gaps due to environmental changes, the spatial distribution has a higher chance of becoming uneven, leading to transformation errors. These errors grow with distance from the benchmark area, increasing alignment inaccuracies across the dataset.

This project presents an indoor navigation system based on image matching, aiming to address the challenges of localization and navigation in indoor environments. The system utilizes Simultaneous Localization and Mapping (SLAM) technology to capture high-resolution images and point cloud data, combined with the VGG16 model from Convolutional Neural Networks (CNN) for image processing, feature extraction, and matching.

In our research, we conducted experiments at the Faculty of Architecture and the Built Environment of Delft University of Technology, using a SLAM scanner to obtain 360-degree panoramic images and point cloud data of the indoor environment. Through cube mapping projection, we converted the panoramic images into six planar views, selecting the front, right, and left views as positioning references. Additionally, we reconstructed the indoor environment structure and designed node networks for positioning and navigation.

The technical architecture of this system comprises three main components: VGG16-based image feature extraction, cosine similarity-based image matching, and DBSCAN algorithm for location clustering. Through this method, the system can achieve real-time localization results after image capture and provide users with optimal paths using the A* navigation algorithm.

Experimental results show that when using single image matching, the system's room localization accuracy reaches 74.65\%. When employing multiple image matching and DBSCAN clustering methods, the accuracy significantly improves. In our final evaluation involving 116 positions, the system successfully matched 111 of these positions to their correct rooms, achieving a localization accuracy of 95.69\%.

This research not only provides an innovative solution for indoor positioning and navigation but also points the way for future research, including support for multi-floor navigation, enhancing CNN model performance, and automating building processing. This technology has the potential for widespread application in complex indoor environments such as large buildings, conference centers, and university campuses, offering users accurate, real-time positioning and navigation services.