Conference Presentations by pileun kim
With the rapid advancement of laser scanning and photogrammetry technologies, geometric data coll... more With the rapid advancement of laser scanning and photogrammetry technologies, geometric data collection at construction sites by contractors has been increased to improve constructability, productivity, and onsite safety. Especially, the latest laser scanning technology provides faster scanning-speed, longer ranges, and higher accuracy and resolutions. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Full scanning without information loss requires that the scanning location should be changed several times, which also leads to extra work for registering each scanned point cloud. Alternatively, this paper presents an autonomous mobile robot which navigates a construction site and continuously updates a progress of 3D scanning with point clouds. This mobile robot system uses the 3D simultaneous localization and mapping (SLAM) technique to determine its navigation paths in an unknown environment, capture the survey-quality RGB mapped point cloud data, and automatically register the scans for geometric reconstruction of a construction site. The performance of the overall system was tested in indoor environments and validated with promising results.
International Symposium on Automation and Robotics in Construction (ISARC), 2016
– The aim of this paper is to introduce a novel method that automatically registers colored 3D po... more – The aim of this paper is to introduce a novel method that automatically registers colored 3D point cloud sets without using targets or any other manual alignment processes. For fully automated point cloud registration without targets or landmarks, our approach utilizes feature detection algorithms used in computer vision. A digital camera and a laser scanner is utilized and the sensor data is merged based on a kinematic solution. The proposed approach is to detect and extract common features not directly from a 3D point cloud but from digital images corresponding to the point clouds. The initial alignment is achieved by matching common SURF features from corresponding digital images. Further alignment is obtained using plane segmentation and matching from the 3D point clouds. The test outcomes show promising results in terms of registration accuracy and processing time.
34rd International Symposium on Automation and Robotics in Construction (ISARC), 2017
– Automated recognition of building elements convey vital information for inspection, monitoring ... more – Automated recognition of building elements convey vital information for inspection, monitoring and maintenance operations in indoor environments. However, existing object recognition methods from point clouds suffer from problems due to sensor noise, occlusion and clutter, which are prevalent in indoor environments. This paper proposes an object recognition method based on thermal-mapped point clouds for building elements consisting of electrical systems and heating, ventilation, and airconditioning (HVAC) components. The proposed processing pipeline involves data collection from a mobile robot using both laser scanners and a thermal camera where temperature mapping can be performed from thermal images to point cloud. Next, the ceiling region containing the building elements of interest is identified and extracted from the point cloud. Segmentation of peak and valley thermal intensity regions is carried out based on absolute and relative temperature threshold values. The identified point cloud clusters can be each associated with a building element and localized based on the cluster center. The proposed building element recognition method was validated with two sets of laser scan data collected in an indoor laboratory. Experimental results for detection of lighting elements and cooling elements showed that the method achieved an average of 100% precision, 90% recall, and 0.25m root mean squared error (RMSE).
International Workshop on Computing for Civil Engineering (IWCCE), 2017
Point cloud-based reconstruction is a crucial process for process monitoring and as-built modelin... more Point cloud-based reconstruction is a crucial process for process monitoring and as-built modeling, but still a challenging task in construction applications. Due to the finite perspective of a scan, multiple scans from different locations are required to obtain full scene coverage. Then, a registration process is required to transform all point clouds into a common coordinate frame. In this paper, a fully automated target-less point cloud registration method is presented. For this approach, a laser scanning device and a co-registered digital camera are required. This method consists of three major steps for an automatic registration of point clouds. First, it detects common features on the corresponding overlapped images from different locations. Second, it achieves coarse registration using the extracted features on images. Lastly, the fine registration is attained by the iterative closet point (ICP) algorithm. The proposed novel method was tested on a real construction site and the test results of automatic registration of all different scan positions were successfully verified in terms of accuracy. INTRODUCTION Both laser scanning and photogrammetry have been highly studied with the recent improvement in sensing technologies. These two technologies have strengths and weaknesses based on working environments and data quality requirements; however, photogrammetry cannot provide the same level of accuracy as laser scanners do in general. In particular, 3D laser scanning technology has been widely used in construction applications to render as-built objects or environments in the form of dense point cloud data. The abundant amount of point cloud data can be used to efficiently model a construction site. A complete point cloud data of the construction site without shading area can be acquired by scanning multiple times in different scanning positions. Each scan position defines a local coordinate system, which means that the point cloud of each viewpoint is referred to this local frame. A common reference system is required to analyze different laser scanning positions. The transformation process of all local coordinate systems into a common reference coordinate system is called registration. Many studies have been conducted on the registration of multi-view point clouds in the past few years. There are several types of point cloud registration have been studied; target-based, ICP-based, and feature-based. Target-based registration is a reliable and precise method, but is time-consuming. Before scanning, the targets must be placed inside the scanning area. After scanning, the targets have to be collected and manually or automatically recognized to define corresponding points for the registration. Becerik-gerber et al. (2011) proposed a target-based point cloud registration method. They experimented with three different types of targets such as fixed paper, paddle, and sphere, and with
Papers by pileun kim
With the rapid advancement of laser scanning and photogrammetry technologies, frequent geometric ... more With the rapid advancement of laser scanning and photogrammetry technologies, frequent geometric data collection at construction sites by contractors has been increased for the purpose of improving constructability, productivity, and onsite safety. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Obtaining a complete scan of a construction site without information loss requires that laser scans are obtained from multiple scanning locations around the site, which also necessitates extra work for registering each scanned point cloud. As an alternate solution to this problem, this paper introduces an autonomous mobile robot which navigates a scan site based on a continuously updated point cloud map. This mobile robot system utilizes the 2D Hector Simultaneous Localization and Mapping (SLAM) technique to estimate real-time positions and orientations of the robot in the x-y plane. Then, the 2D localization information is used to create 3D point clouds of unknown environments in real time to determine its navigation paths as a pre-scanning process. The advantage of this framework is the ability to determine the optimal scan position and scan angle to reduce the scanning time and effort for gathering high resolution point cloud data in real-time. The mobile robot system is able to capture survey-quality RGB-mapped point cloud data, and automatically register the scans for geometric reconstruction of the site. The performance of the overall system was tested in an indoor environment and validated with promising results.
International Journal of Intelligent Robotics and Applications., 2017
Many of the civil structures are more than half way through or nearing their intended service lif... more Many of the civil structures are more than half way through or nearing their intended service life; frequently assessing and maintaining structural integrity is a top maintenance priority. Robotic inspection technologies using ground and aerial robots with 3D scanning and imaging capabilities have the potential to improve safety and efficiency of infrastructure management. To provide more valuable information to inspectors and agency decision makers, automatic environment sensing and semantic information extraction are fundamental issues in this field. This paper introduces an innovative method for generating thermal-mapped point clouds of a robot’s work environment and performing automatic object recognition with the aid of thermal data fused to 3D point clouds. The laser scanned point cloud and thermal data were collected using a customdesigned mobile robot. The multimodal data was combined with a data fusion process based on texture mapping. The automatic object recognition was performed by two processes: segmentation with thermal data and classification with scanned geometric features. The proposed method was validated with the scan data collected in an entire building floor. Experimental results show that the thermal integrated object recognition approach achieved better performance than a geometry only-based approach, with an average recognition accuracy of 93%, precision of 83%, and recall rate of 86% for objects in the tested environment including humans, display monitors and light fixtures.
ASCE Journal of Computing in Civil Engineering, 2017
Due to the limited view of each single laser scan data, multiple scans are required to cover all ... more Due to the limited view of each single laser scan data, multiple scans are required to cover all scenes of the large construction site, and a registration process is needed to merge them together. While many research efforts have been made on the automatic point cloud registration, however the prior works have some limitations; the automatic registration was tested in a bounded region and required a large overlapped area between scans. The aim of this paper is to introduce a novel method that achieves the automatic point cloud registration in an unbounded region and with a relatively small overlapped area without using artificial targets, landmarks, or any other manual alignment process. For the automatic point cloud registration, the proposed framework utilizes the
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Conference Presentations by pileun kim
Papers by pileun kim