Towards Object Driven Floor Plan Extraction From Laser Point Cloud

Abstract

ABSTRACT:During the last years, the demand for indoor models has increased for various purposes. As a provisional step to proceed towards higher dimensional indoor models, powerful and flexible floor plans can be utilised. Therefore, several methods have been proposed that provide automatically generated floor plans from laser point clouds. The prevailing methodology seeks to attain semantic enhancement of a model (e.g. the identification and labelling of its components) built upon already reconstructed (a priori) geometry. In contrast, this paper demonstrates preliminary research on the possibility to directly incorporate semantic knowledge, which is itself derived from the raw data during the extraction, into the geometric modelling process. In this regard, we propose a new method to automatically extract floor plans from raw point clouds. It is based on a hierarchical space partitioning of the data, integrated with primitive selection actuated by object detection. First, planar primitives corresponding to vertical architectural structures are extracted using M-estimator SAmple and Consensus (MSAC). The set of the resulting line segments are refined by a selection process through a novel door detection algorithm, considering optimization of prior information and fitness to the data. The selected lines are used as hyperlines to partition the space into enclosed areas. Finally, a floor plan is extracted from these partitions by Minimum Description Length (MDL) hypothesis ranking. The algorithm is applied on a real mobile laser scanner dataset and the results are evaluated both in terms of door detection and consecutive floor plan extraction.

Automated Storey Separation and Door and Window Extraction for Building Models from Complete Laser Scans

“…The problem of door and window detection has not been explored in as much depth as other elements of indoor 3D building modeling and has only been examined on a dataset specific basis. As noted by Babacan et al [17], door detection has only arisen in very recent studies. The detection of doors, extremely common indoor building elements, is useful for understanding the environmental structure in order to perform efficient navigation or to plan appropriate evacuation routes.…”

“…Furthermore, the diagonal representations of an open door in the final point cloud have been used in previous work, including [17], to help identify doors. The method presented in [17] investigates the connectivity of the extracted line segments and uses the anchor position of an open door to help identify one edge of a doorway. Most similar to the method developed in this work is the computation of wall volumes presented in [19].…”

“…Previous work, including [32][33][34], used a truth model to evaluate the success of their building modelling methodologies. In the work presented here, the quality of the detected windows and doors will also be evaluated in detail by comparing the calculated dimensions of the doors and windows to the true dimensions of the doors and windows, following examples of precision and recall analysis from [17,[24][25][26].…”

Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach

“…The most common methods to gather status data are laser scanning or photogrammetry [8], and their output is point cloud data. However, the point cloud data typically includes unnecessary objects, such as people, tools, and materials, which are considered clutter in the point cloud data [9,10]. These clutter objects can negatively impact the accuracy and speed of automated as-built BIM creation, particularly in terms of point-cloud semantic segmentation of building elements [11,12].…”

“…The most typical method for removing indoor clutter objects is the line-fitting-based method. The line-fitting-based approach identifies outliers using obtained lines or planes that represent the additional elements that need to be preserved [9,10,[14][15][16]. However, the line-fitting-based method often ignores certain types of elements, such as indoor columns or walls, depending on their parameter values defined a priori, and it may not accurately reflect the thickness of inner walls [14,16].…”