3D modelling method and application to a digital campus by fusing point cloud data and image data

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

This paper presents a new algorithm for building an indoor map by integrating point clouds of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest point (ICP) algorithm is one of the well-known methods for such purpose and often used for mobile robot SLAM. However, the algorithm is designed based on dense (or continuous) measurement of the same space with known relative positions of measurement points and angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR locations are unknown. Such situations are seen when some installed LIDARs in a room are used to build a background indoor map for object tracking, or mobile LIDARs are used by technicians to build a digital indoor map, where each space is captured only at a few locations with different angles. To tackle this issue, our method extracts line segments and edge points as features from given LIDAR point clouds and finds shape coincidences commonly contained in a pair of given point clouds to identify positional relationships between LIDARs. By this information, these point clouds can be integrated into common 2D coordinates. Indoor map generation is realized by sequentially applying this integration procedure to every pair of point clouds. Also, the experiments on real data show that our method can identify the relative positions of LIDARs with 10cm-order errors in average, and by sequentially applying the point-cloud integration, the generated maps have only 3% errors.

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR Byeongjun Oh, Minju Kim, Chanwoo Lee, Hunhee Cho and Kyung-In Kang Pages 906-912 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: LiDAR (Light Detection and Ranging) emerges as a mapping technology that provides fast, accurate, and reliable data with geometric representation of construction facilities. The technology has received high recognition especially since the importance of digital transformation in construction projects has been emphasized. However, the current state of 3D technologies including LiDAR encounters difficulty in recognizing and extracting accurate information on the as-built status of the buildings and construction sites. As a preliminary study for the development of a 3D architectural geometric representation of building and environment, this paper proposes a 3D modeling approach based on point cloud data obtained by LiDAR technology. This paper suggests a novel method for data acquisition on as-built status of construction sites and buildings during construction-operation phases. Furthermore, a field test has been performed for visualizing and modeling of the indoors of a residential house. The results of this paper are expected to inaugurate adaptation of LiDAR technology during the as-built process, and further implementation of digital technology after construction phase in construction projects. Keywords: LiDAR; 3D modeling; building construction; digital transformation; point cloud data DOI: https://doi.org/10.22260/ISARC2019/0121 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

Light detection and ranging (LiDAR) sensors provide accurate 3-D point clouds for noncooperative spacecraft pose estimation. Several robust methods, such as iterative closest point, exist to perform a local refinement of the pose starting from an initial estimate. However, finding the initial pose of the spacecraft is a global optimization problem, which is challenging to solve in real time. This is especially true on space hardware with limited computing power. In addition, many spacecrafts have a shape with multiple symmetries, making an unambiguous initial pose estimation impossible. This work introduces a convolutional-neural-network-based pose estimation method, accounting for potential symmetries of the target satellite. The point clouds are projected to a 2-D depth image before being processed by the network. To generate a sufficient amount of training data, a LiDAR simulator integrating multiple effects such as reflections or laser beam divergence is developed. While being trained solely on synthetic point clouds, the pose estimation method shows to be precise, efficient, and reliable when evaluated on real point clouds taken at a hardware-in-the-loop rendezvous test facility. A runtime evaluation on potential space computing hardware is also performed to demonstrate the applicability of the method to real-time onboard pose estimation.

Airborne light detection and ranging (LiDAR) data are increasingly used in various fields such as topographic mapping, urban planning, and emergency management. A necessary processing step in the application of airborne LiDAR data is the elimination of mismatch errors. This paper proposes a new method for airborne LiDAR strip adjustment based on point clouds with planar neighborhoods; this method is intended to eliminate errors in airborne LiDAR point clouds. Initially, standard pre-processing tasks such as denoising, ground separation, and resampling are performed on the airborne LiDAR point clouds. Subsequently, this paper introduces a unique approach to extract point clouds with planar neighborhoods which is designed to enhance the registration accuracy of the iterative closest point (ICP) algorithm within the context of airborne LiDAR point clouds. Following the registration of the point clouds using the ICP algorithm, tie points are extracted via a point-to-plane projection method. Finally, a strip adjustment calculation is executed using the extracted tie points, in accordance with the strip adjustment equation for airborne LiDAR point clouds that was derived in this study. Three sets of airborne LiDAR point cloud data were utilized in the experiment outlined in this paper. The results indicate that the proposed strip adjustment method can effectively eliminate mismatch errors in airborne LiDAR point clouds, achieving a registration accuracy and absolute accuracy of 0.05 m. Furthermore, this method’s processing efficiency was more than five times higher than that of traditional methods such as ICP and LS3D.

Integration of photogrammetry and light detection and ranging (LiDAR) algorithms has garnered attention to enhance visual fidelity and geometric accuracy in three-dimensional (3D) modeling. Here we show an ultralight 10 g solid-state single-photon LiDAR that minimizes photon cost per measurement. Achieving a maximum indoor distance of 25 m and a point cloud density of ~3.16 Mpps, the LiDAR provides geometric fidelity, while maintaining fine structure information challenging for conventional LiDAR. Key embedded components include a Q-switching semiconductor laser, which emits a 50-ps pulse-width tail-free laser using bandgap renormalization. A four-channel time-to-digital converter achieves a 3 ps timing jitter per channel and features real-time time-walk error correction for Poisson-distributed photon counts. A low-Q two-dimensional (2D) MEMS mirror with a 20 mm2 mirror size and precisely controlled feedforward-driven frequency enables non-repetitive scanning and super-dense point cloud generation. We present 3D modeling using the colored point clouds and discuss its characteristics and challenges.

With the rapid expansion of railway networks globally, ensuring rail infrastructure safety through efficient detection methods has become critical. Traditional inspection systems face limitations in flexibility, adaptability to adverse weather, and multifunctional integration. This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. The system employs a lightweight rail inspection vehicle equipped with dual LiDARs and an Astro camera, synchronized with an unmanned aerial vehicle (UAV) carrying industrial-grade LiDAR. We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. For intrusion detection, a You Only Look Once version 3 (YOLOv3)-ResNet fusion model achieves a recall rate of 0.97 and precision of 0.99. The system's innovative design and technical implementation offer significant improvements in railway track inspection efficiency and safety. This work establishes a new paradigm for adaptive railway maintenance in complex environments.

With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.

A 3D surface modeling format that is commonly used today is point cloud. 3D surface model segmentation could provide data for analysis in various fields. In the context of Geographic Information Systems, point cloud data obtained from the Light Detection and Ranging (LiDAR) sensor are used by machines to automatically identify objects such as houses, buildings, land, and rivers. There has been many Deep Learning approach through Convolutional Neural Network (CNN) that has been proven to be very capable for 2-dimensional imagery classification and segmentation. PointNet is a Deep Learning architecture that is designed so that the point cloud format that is still tabular form, can be directly convoluted by the CNN model. In this study, an improvement of PointNet is proposed for Point Cloud data of Kupang City. The Point Cloud data were acquired using an Unmanned Aerial Vehicle with a LiDAR sensor installed. The data were pre-processed and divided into training and testing data. The data were processed with the PointNet architecture and the model was tested using several metrics. The experiment shows that the PointNet architecture is capable on segmenting Geographical Point Cloud Data. In addition, incorporating voxel's color features could increase the performance of the segmentation.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Reduction is a crucial stage in the surgical treatment of bone fractures.The detailed fracture information of the patient can be obtained from computed tomography (CT) scans before surgery and enable physicians to plan preoperative reduction, to reduce the operation time and thus increase the probability of getting satisfactory results. The primary purpose of this paper is to design a computer-aided automatic registration method of fracture point cloud data, so as to simplify the fracture reduction process. In this paper, we propose an integrated fracture reduction system was introduced. The system enables direct semi-automatic processing from CT images to fracture reduction. First, a 3D fracture models is reconstructed from CT images by using the modified Marching Cube (MC) algorithm and is discretized to generate a point cloud. Second, the K-dimensional (KD) tree algorithm is used to cluster and segment the point clouds to identify different fracture fragments. Last, through the combination algorithm of Normal Distributions Transform(NDT) and modified Iterative Closest Point(ICP), the coarse alignment and fine registration of point clouds are achieved step by step. This method has been successfully applied to the reduction of tibial fracture. In the tests performed, the processing time of each step, the point cloud and the 3D model after registration are displayed. The semi-automatic integrated system based on preoperative CT scanning is used to realize fracture reduction，which provides a feasible foundation for minimally invasive and accurate fracture reduction surgery.

Topographic surveys are conducted to identify elevation features of the areas in which construction projects are planned to be built. The use of an Unmanned Aerial Vehicle (UAV) equipped with a Light Detection and Ranging (LiDAR) sensor array can improve the current method in obtaining topographic models. However, the development of UAV is usually costly, so hardware-in-loop (HIL) or software-in-loop (SIL) approaches are usually adopted first. AirSim is widely used simulation software used in HIL or SIL for UAV development. However, the simulator does not have a compact concept for 3D scanning to create a 3D topographic map. This paper develops the 3D scanning method and validates it in the virtual environment. The scanning method reads data collected from LiDAR sensor and then processes it into point clouds before constructing it into 3D objects. To validate and verify the 3D model, results collected from the avatar UAV in the simulator are compared with the actual objects. Results show that the proposed approach is accurate with less than 5% error in the measurement.KeywordsUnmanned aerial vehicle (UAV)Light detection and ranging (LiDAR)Aerial informatics and robotics simulation (AirSim)3D topographic map3D model

Robust multi-task learning network for complex LiDAR point cloud data preprocessing

Chapter 5 - Data Preprocessing and Feature Extraction

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.