The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

Using digital design models has been a common practice in the manufacturing industry for decades. Project teams at companies such as Boeing and Toyota have placed digital models at the core of their collaborative, concurrent engineering processes. The same approach, called building information modeling (BIM), is increasingly being adopted by architecture, engineering, and construction (AEC) service providers for building and infrastructure projects. Unlike CAD, which uses software tools to generate digital 2D and/or 3D drawings, BIM facilitates a new way of working: creating designs with intelligent objects that enables cross-functional project teams in the building and infrastructure industries to collaborate in a way that gives all stakeholders a clearer vision of the project. Models created using software for BIM are intelligent because of the relationships and information that are automatically built into the model. Components within the model know how to act and interact with one another. BIM not only enables engineers architects and construction firms to work more efficiently, but creates a foundation for sustainable design, enabling designers to optimize the environmental footprint of a structure during the design phase. Convergence is breaking down the barriers between technical disciplines. The integration of BIM, geospatial, physical modeling and 3D visualization provides a framework of interoperability that enables an intelligent synthetic model of entire urban environments.

Building information modeling

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification.

Thomas Czerniawski

Papers

Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds

6D DBSCAN-based segmentation of building point clouds for planar object classification

Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor

Automated digital modeling of existing buildings: A review of visual object recognition methods

Automated segmentation of RGB-D images into a comprehensive set of building components using deep learning