fast indoor mapping to feed an indoor db for building and facility management

Abstract. Facility Management activities require to collect and organize a large amount of information about a building as, for example, geometry, MEP structures, lighting and antifire devices, typologies of furniture, paving characteristics, structures and more. Nowadays the data acquisition procedures for indoor environments are usually still carried on with old style approach, where surveyors have to manually map and acquire the data, walking along the sites with a poor level of digitalization The success story presented in the paper describes how using an Indoor Mobile Mapping approach (Zlot et al., 2014), it is possible to satisfy the need to acquire plant views of a large parts of buildings and, simultaneously, to record a 3D+Full resolution RGB images. Thanks to this fast acquisition it is later possible to feed a 2D/3D database, identifying the main objects needed to support a facility management process. The iMMS that has been used is based on SLAM approach, that allows the user to map and survey large sites also indoor, that means without the presence of GNSS signal and without the use of accurate and expense IMU devices. The data acquired in the field has been process with standard/commercial software that is usually used to create DB for outdoor mobile mapping.</p

Semantic alignment of LiDAR data at city scale

on the left show overhead maps of the car trajectories. The third image shows the original alignments provided by Google. The rightmost image shows our alignment. Different colors represent different scans of the same surfaces. This paper describes an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments. The problem is challenging because global pose estimation techniques (GPS) do not work well in city environments with tall buildings, and local tracking techniques (integration of inertial sensors, structure-from-motion, etc.) provide solutions that drift over long ranges, leading to solutions where data collected over wide ranges is warped and misaligned by many meters. Our approach to address this problem is to extract “seman-tic features ” with object detectors (e.g., for facades, poles, cars, etc.) that can be matched robustly at different scales, and thus are selected for different iterations of an ICP algo-rithm. We have implemented an all-to-all, non-rigid, global alignment based on this idea that provides better results than alternatives during experiments with data from larg

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

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