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Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 42363

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Special Issue Editor

Dr. Petri Rönnholm

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Guest Editor

Department of Built Environment, Aalto University, P.O. Box 14100, 00076 Aalto, Finland
Interests: laser scanning; photogrammetry; mobile mapping; registration; data integration; digital image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The need for accurate mapping of our built environment increases. For example, modern navigation applications, augmented reality, engineering tasks, and building information models require accurate 3D information. In many cases, data are also needed from areas that have poor or non-existing satellite visibility, which causes challenges to systems that rely on direct georeferencing sensors. Simultaneous localization and mapping systems have appeared in the markets, challenging traditional mapping processes. In addition, visual odometry has appeared to support or replace traditional direct georeferencing systems. Automation in both data acquisition and data processing can make mapping processes more efficient. New mapping devices, data processing methods, applications, and more efficient mapping processes are constantly being developed. In this Special Issue, we will compile state-of-the-art research that addresses various aspects of terrestrial and mobile mapping, which allow for the modeling of complex indoor and outdoor environments. Potential topics include, but are not limited to, the following:

Novel mobile mapping devices;
Accuracy estimation of terrestrial and mobile mapping;
Improvement of terrestrial and mobile mapping accuracy;
Novel mapping processes;
Automation in mapping processes;
Indoor mapping challenges;
Seamless data from outdoor to indoor;
Novel applications of highly accurate 3D data.

Prof. Petri Rönnholm
Guest Editor

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Keywords

Mobile mapping
Indoor mapping
Backpack mapping
Terrestrial laser scanning
Mobile laser scanning
Simultaneous localization and mapping
Photogrammetry
Structure-from-motion
360° photography
HD mapping
Accuracy
Georeferencing
Data processing
3D as-built
Indoor drones

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Published Papers (10 papers)

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20 pages, 21111 KiB

Open AccessArticle

Restoration of Individual Tree Missing Point Cloud Based on Local Features of Point Cloud

by Wei Cao, Jiayi Wu, Yufeng Shi and Dong Chen

Remote Sens. 2022, 14(6), 1346; https://doi.org/10.3390/rs14061346 - 10 Mar 2022

Cited by 8 | Viewed by 2737

Abstract

LiDAR (Light Detection And Ranging) technology is an important means to obtain three-dimensional information of trees and vegetation. However, due to the influence of scanning mode, environmental occlusion and mutual occlusion between tree canopies and other factors, a tree point cloud often has different degrees of data loss, which affects the high-precision quantitative extraction of vegetation parameters. Aiming at the problem of a tree laser point cloud being missing, an individual tree incomplete point cloud restoration method based on local features of the point cloud is proposed. The L₁-Median algorithm is used to extract key points of the tree skeleton, then the dominant direction of skeleton key points and local point cloud density are calculated, and the point cloud near the missing area is moved based on these features to gradually complete the incomplete point cloud compensation. The experimental results show that the above repair method can effectively repair the incomplete point cloud with good robustness and can adapt to the individual tree point cloud with different geometric structures and correct the branch topological connection errors. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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23 pages, 3328 KiB

Open AccessArticle

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

by Jingxue Bi, Hongji Cao, Yunjia Wang, Guoqiang Zheng, Keqiang Liu, Na Cheng and Meiqi Zhao

Remote Sens. 2022, 14(2), 297; https://doi.org/10.3390/rs14020297 - 10 Jan 2022

Cited by 10 | Viewed by 2342

Abstract

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m². In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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35 pages, 19653 KiB

Open AccessArticle

Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation

by Raja Manish, Seyyed Meghdad Hasheminasab, Jidong Liu, Yerassyl Koshan, Justin Anthony Mahlberg, Yi-Chun Lin, Radhika Ravi, Tian Zhou, Jeremy McGuffey, Timothy Wells, Darcy Bullock and Ayman Habib

Remote Sens. 2022, 14(1), 231; https://doi.org/10.3390/rs14010231 - 5 Jan 2022

Cited by 15 | Viewed by 6221

Abstract

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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14 pages, 7672 KiB

Open AccessArticle

Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios

by Ursula Kälin, Louis Staffa, David Eugen Grimm and Axel Wendt

Remote Sens. 2022, 14(1), 90; https://doi.org/10.3390/rs14010090 - 25 Dec 2021

Cited by 6 | Viewed by 3739

Abstract

To validate the accuracy and reliability of onboard sensors for object detection and localization for driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle during test maneuvers within a reference environment (e.g., car during parking maneuvers), independent of the onboard sensors. One requirement is a 6 degree of freedom (DoF) pose with position uncertainty below 5 mm (3σ), orientation uncertainty below 0.3° (3σ), at a frequency higher than 20 Hz, and with a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, synchronization via a Precision Time Protocol (PTP) and system interoperability to a robot operating system (ROS) are achieved. The developed system combines motion capture cameras mounted in a 360° panorama view setup on the vehicle, measuring retroreflective markers distributed over the test site with known coordinates, while robotic total stations measure a prism on the vehicle. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is visualized. The results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met while allowing a flexible installation in different environments. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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18 pages, 5859 KiB

Open AccessArticle

An Efficient Approach to Automatic Construction of 3D Watertight Geometry of Buildings Using Point Clouds

by Yuanzhi Cai and Lei Fan

Remote Sens. 2021, 13(10), 1947; https://doi.org/10.3390/rs13101947 - 17 May 2021

Cited by 17 | Viewed by 3513

Abstract

Recent years have witnessed an increasing use of 3D models in general and 3D geometric models specifically of built environment for various applications, owing to the advancement of mapping techniques for accurate 3D information. Depending on the application scenarios, there exist various types of approaches to automate the construction of 3D building geometry. However, in those studies, less attention has been paid to watertight geometries derived from point cloud data, which are of use to the management and the simulations of building energy. To this end, an efficient reconstruction approach was introduced in this study and involves the following key steps. The point cloud data are first voxelised for the ray-casting analysis to obtain the 3D indoor space. By projecting it onto a horizontal plane, an image representing the indoor area is obtained and is used for the room segmentation. The 2D boundary of each room candidate is extracted using new grammar rules and is extruded using the room height to generate 3D models of individual room candidates. The room connection analyses are applied to the individual models obtained to determine the locations of doors and the topological relations between adjacent room candidates for forming an integrated and watertight geometric model. The approach proposed was tested using the point cloud data representing six building sites of distinct spatial confirmations of rooms, corridors and openings. The experimental results showed that accurate watertight building geometries were successfully created. The average differences between the point cloud data and the geometric models obtained were found to range from 12 to 21 mm. The maximum computation time taken was less than 5 min for the point cloud of approximately 469 million data points, more efficient than the typical reconstruction methods in the literature. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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32 pages, 28675 KiB

Open AccessArticle

Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping

by Raja Manish, Yi-Chun Lin, Radhika Ravi, Seyyed Meghdad Hasheminasab, Tian Zhou and Ayman Habib

Remote Sens. 2021, 13(2), 276; https://doi.org/10.3390/rs13020276 - 14 Jan 2021

Cited by 17 | Viewed by 3651

Abstract

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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17 pages, 20041 KiB

Open AccessArticle

Test Charts for Evaluating Imaging and Point Cloud Quality of Mobile Mapping Systems for Urban Street Space Acquisition

by Norbert Pfeifer, Johannes Falkner, Andreas Bayr, Lothar Eysn and Camillo Ressl

Remote Sens. 2021, 13(2), 237; https://doi.org/10.3390/rs13020237 - 12 Jan 2021

Cited by 5 | Viewed by 2921

Abstract

Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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31 pages, 24359 KiB

Open AccessArticle

Evaluating the Quality of TLS Point Cloud Colorization

by Arttu Julin, Matti Kurkela, Toni Rantanen, Juho-Pekka Virtanen, Mikko Maksimainen, Antero Kukko, Harri Kaartinen, Matti T. Vaaja, Juha Hyyppä and Hannu Hyyppä

Remote Sens. 2020, 12(17), 2748; https://doi.org/10.3390/rs12172748 - 25 Aug 2020

Cited by 18 | Viewed by 6800

Abstract

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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21 pages, 4373 KiB

Open AccessArticle

A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modeling

by Matias Ingman, Juho-Pekka Virtanen, Matti T. Vaaja and Hannu Hyyppä

Remote Sens. 2020, 12(16), 2624; https://doi.org/10.3390/rs12162624 - 14 Aug 2020

Cited by 18 | Viewed by 6283

Abstract

The automated 3D modeling of indoor spaces is a rapidly advancing field, in which recent developments have made the modeling process more accessible to consumers by lowering the cost of instruments and offering a highly automated service for 3D model creation. We compared the performance of three low-cost sensor systems; one RGB-D camera, one low-end terrestrial laser scanner (TLS), and one panoramic camera, using a cloud-based processing service to automatically create mesh models and point clouds, evaluating the accuracy of the results against a reference point cloud from a higher-end TLS. While adequately accurate results could be obtained with all three sensor systems, the TLS performed the best both in terms of reconstructing the overall room geometry and smaller details, with the panoramic camera clearly trailing the other systems and the RGB-D offering a middle ground in terms of both cost and quality. The results demonstrate the attractiveness of fully automatic cloud-based indoor 3D modeling for low-cost sensor systems, with the latter providing better model accuracy and completeness, and with all systems offering a rapid rate of data acquisition through an easy-to-use interface. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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14 pages, 2882 KiB

Open AccessTechnical Note

An Efficient Filtering Approach for Removing Outdoor Point Cloud Data of Manhattan-World Buildings

by Lei Fan and Yuanzhi Cai

Remote Sens. 2021, 13(19), 3796; https://doi.org/10.3390/rs13193796 - 22 Sep 2021

Cited by 5 | Viewed by 2803

Abstract

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed. Full article

(This article belongs to the Special Issue Terrestrial and Mobile Mapping in Complex Indoor and Outdoor Environments)

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