Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

Most existing approaches to characterizing thermal properties of buildings and heat emissions from their elements rely on manual inspection and as such are slow, and labor intensive. This is often a daunting task, which requires several days of on-site inspection. In this paper, we propose a fully automatic approach to construct a 3D thermal point cloud of the building interior reflecting the geometry including walls, floors, and ceilings, as well as structures such as furniture, lights, windows, and plug loads. Our approach is based on a wearable ambulatory backpack comprising multiple sensors such as Light Detection And Ranging (LiDAR) scanners, and Infrared and optical cameras. As the operator wearing the backpack walks through the building, the LiDAR scans are collected and processed in order to compute the 3D geometry of the building. Furthermore, the Infrared cameras are calibrated intrinsically and extrinsically such that the captured images are registered to the captured geometry. Thus, the temperature data in the Infrared images is associated with the geometry resulting in a “thermal 3D point cloud”. The same process can be repeated using optical imagery resulting in a “visible 3D point cloud”. By visualizing the two point clouds simultaneously in interactive rendering tools, we can virtually walk through the thermal and optical 3D point clouds, toggle between them, identify and annotate, “hot ” regions, objects, plug loads, thermal and moisture leaks, and document their location with fine spatial granularity in the 3D point clouds.

Automated 3D modeling of building interiors is used in applications such as virtual reality and environment mapping. Texturing these models allows for photo-realistic visualizations of the data collected by such modeling systems. While data acquisition times for mobile mapping systems are considerably shorter than for static ones, their recovered camera poses often suffer from inaccuracies, resulting in visible discontinuities when successive images are projected onto a surface for texturing. We present a method for texture mapping models of indoor environments that starts by selecting images whose camera poses are well-aligned in two dimensions. We then align images to geometry as well as to each other, producing visually consistent textures even in the presence of inaccurate surface geometry and noisy camera poses. Images are then composited into a final texture mosaic and projected onto surface geometry for visualization. The effectiveness of the proposed method is demonstrated on a number of different indoor environments.