Indoor localisation through Isovist fingerprinting from point clouds and floor plans

The objective of this paper is to investigate and propose a method for Indoor Localisation based on Isovists, with the aim of extending the fields of Location-based Services and Geomatics. Various methods and combinations incorporating Isovist concepts, Space Syntax, and visibility graphs are examined and assessed. By investigating these...

Indoor localisation and location tracking in indoor facilities based on LiDAR point clouds and images of the ceilings

Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the...

Organizing and visualizing point clouds with continuous levels of detail

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower...

Game Engine-based Point Cloud Visualization and Perception for Situation Awareness of Crisis Indoor Environments

Because unknown interior layouts can have serious consequences in time-sensitive situations, crisis response teams request many potential solutions for visualizing indoor environments in crisis scenarios. This research uses a game engine to directly visualize point cloud data input of indoor environments for generating clear interaction between...

A comparative study of point clouds semantic segmentation using three different neural networks on the railway station dataset

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...

An Approach to Map Visibility in The Built Environment from Airborne LiDAR Point Clouds

Sustainable development can only be achieved with an innovative improvement from the way we currently analyze, design, build and manage our urban spaces. Current digital analysis and design methods for cities, such as visibility analysis, deeply rely on mapping and modeling techniques. However, most methods fall short of depicting the real...

Automatic detection and characerization of ground occlusions in urban point clouds from mobile laser scanning data

Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have...

HistSFC: Optimization for nD massive spatial points querying

Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B+- tree could be utilized. Based on the basic structure, this paper develops a generic HistSFC approach which utilizes a histogram tree recording...

An optimized SFC approach for nD window querying on point clouds

Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but most of them concentrate on low dimensional spaces. High...

Towards 10^15-level point clouds management - a nD PointCloud structure

Drastically increasing production of point clouds as well as modern application fields like robotics and virtual reality raises essential demand for smart and highly efficient data management. Effective tools for the managing and direct use of large point clouds are missing. Current state-of-the-art database management systems (DBMS) present...

Management of large indoor point clouds: An initial exploration

Indoor navigation and visualization become increasingly important nowadays. Meanwhile, the proliferation of new sensors as well as the advancement of data processing provide massive point clouds to model the indoor environment in high accuracy. However, current state-of-the-art solutions fail to manage such large datasets efficiently. File...

Point Cloud Based Visibility Analysis: first experimental results

Visibility computed from a LiDAR point cloud offers several advantages compared to using a gridded digital height-model. With a higher resolution and detailed information, point cloud data can provide precise analysis as well as an opportunity to avoid the process of generating a surface representation or a solid model. Also a better inclusion...

Strategies to evaluate the visibility along an indoor path in a point cloud representation

Many research works have been oriented to the formulation of different algorithms for estimating the paths in indoor environments from three-dimensional representations of space. The architectural configuration, the actions that take place within it, and the location of some objects in the space influence the paths along which is it possible...

Indoor A* Pathfinding through an Octree Representation of a Point Cloud

There is a growing demand of 3D indoor pathfinding applications. Researched in the field of robotics during the last decades of the 20th century, these methods focussed on 2D navigation. Nowadays we would like to have the ability to help people navigate inside buildings or send a drone inside a building when this is too dangerous for people....