scholarly journals Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2105
Author(s):  
Hannah M. Cooper ◽  
Thad Wasklewicz ◽  
Zhen Zhu ◽  
William Lewis ◽  
Karley LeCompte ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Cost Benefit ◽  
Point Clouds ◽  
Measurement Unit ◽  
Unmanned Aerial Systems ◽  
Scanning System ◽  
High Definition ◽  
Topographic Complexity ◽  
Actual Surface ◽  
Test Location

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

scholarly journals Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 555 ◽  
Author(s):  
Erik Heinz ◽  
Christoph Holst ◽  
Heiner Kuhlmann ◽  
Lasse Klingbeil
Keyword(s):  
Reference Point ◽  
Laser Scanning ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Lever Arm ◽  
Laser Scanning System ◽  
The Impact ◽  
Scanning Systems

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

scholarly journals Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 645
Author(s):  
Qian Wang ◽  
Chao Tang ◽  
Cuijun Dong ◽  
Qingzhou Mao ◽  
Fei Tang ◽  
...  
Keyword(s):  
Coordinate System ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Starting Point ◽  
Inspection Work ◽  
Subway Tunnels

When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS’s dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing–Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5–6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

scholarly journals A GNSS-FREE UNMANNED AERIAL LASER SCANNING SYSTEM

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 83-90
Author(s):  
M. Shahbazi ◽  
C. Cortes ◽  
P. Ménard ◽  
J. S. Bilodeau
Keyword(s):  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Measurement Unit ◽  
Scanning System ◽  
Dual Frequency ◽  
Gnss Receiver ◽  
Intrinsic Parameters ◽  
The Individual

Abstract. In this paper, the procedure of developing and evaluating a UAV-borne mapping system is described. The system is equipped with both a LiDAR and a camera. The system mounting parameters, as well as the intrinsic parameters of the individual sensors, are calibrated rigorously. Simultaneous calibration of the LiDAR intrinsic parameters and the LiDAR-camera mounting parameters is performed in a self-calibrating bundle adjustment with additional relative orientation constraints. A visual-inertial approach is proposed to georeference the laser scans without using a GNSS receiver. This approach is motivated not only by the interest of users in low-cost systems but also by the fact that the integrity of GNSS signals might be affected under several environmental conditions, e.g., indoors, in urban canyons, under tree canopies. It is shown that a low-cost inertial measurement unit not equipped with a dual-frequency, real-time kinematic GNSS receiver is still useful for georeferencing the laser scanning data with cm-level accuracy. The scans are also textured using the images captured by the camera, which enriches the LiDAR point clouds with spectral information.

scholarly journals LOW-COST WHEELED ROBOT-BORNE LASER SCANNING SYSTEM FOR INDOOR AND OUTDOOR 3D MAPPING APPLICATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1155-1159
Author(s):  
W. Wu ◽  
C. Chen ◽  
Y. Cong ◽  
Z. Dong ◽  
J. Li ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Outdoor Scenes ◽  
Wheeled Robot ◽  
Laser Scanning System ◽  
Indoor And Outdoor

<p><strong>Abstract.</strong> Aiming to accomplish automatic and real-time three-dimensional mapping in both indoor and outdoor scenes, a low-cost wheeled robot-borne laser scanning system is proposed in this paper. The system includes a laser scanner, an inertial measurement unit, a modified turtlebot3 two-wheel differential chassis and etc. To achieve a globally consistent map, the system performs global trajectory optimization after detecting the loop closure. Experiments are undertaken in two typical indoor/outdoor scenes that is an underground car park and a road environment in the campus of Wuhan University. The point clouds acquired by the proposed system are quantitatively evaluated by comparing the derived point clouds with the ground truth data collected by a RIEGL VZ 400 laser scanner. The results present an accuracy of 90% points below 0.1 meter error in the tested scene, showing that its applicability and potential in indoor and mapping applications.</p>

scholarly journals Accurate Calibration of a Self-Developed Vehicle-Borne LiDAR Scanning System

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ming Guo ◽  
Bingnan Yan ◽  
Tengfei Zhou ◽  
Deng Pan ◽  
Guoli Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Measurement Data ◽  
Point Clouds ◽  
Calibration Method ◽  
Linear Interpolation ◽  
Measurement Unit ◽  
Scanning System ◽  
High Precision Measurement ◽  
Distance Ratio

To obtain high-precision measurement data using vehicle-borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least-squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

scholarly journals PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1709-1713
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. El-Sheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Building Management ◽  
Common Framework

<p><strong>Abstract.</strong> This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.</p>

scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

scholarly journals Feasibility of Mobile Laser Scanning towards Operational Accurate Road Rut Depth Measurements

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1180
Author(s):  
Aimad El Issaoui ◽  
Ziyi Feng ◽  
Matti Lehtomäki ◽  
Eric Hyyppä ◽  
Hannu Hyyppä ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Laser Scanning ◽  
Satellite System ◽  
Measurement Unit ◽  
High Definition ◽  
Depth Measurement ◽  
Laser Scanners ◽  
Fully Automatic ◽  
Global Navigation Satellite ◽  
Automatic Algorithm

This paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from high-definition maps to autonomous car navigation and digitalization of street environments over time and in space.

scholarly journals A LIGHT-WEIGHT LASER SCANNER FOR UAV APPLICATIONS

2016 ◽  
Vol XLI-B1 ◽  
pp. 711-715 ◽  
Author(s):  
A. M. G. Tommaselli ◽  
F. M. Torres
Keyword(s):  
Laser Scanning ◽  
Cost Benefit ◽  
Ground Control ◽  
Raspberry Pi ◽  
Weight System ◽  
Scanning System ◽  
Light Weight ◽  
Control Points ◽  
Ground Control Points ◽  
Flight Height

Unmanned Aerial Vehicles (UAV) have been recognized as a tool for geospatial data acquisition due to their flexibility and favourable cost benefit ratio. The practical use of laser scanning devices on-board UAVs is also developing with new experimental and commercial systems. This paper describes a light-weight laser scanning system composed of an IbeoLux scanner, an Inertial Navigation System Span-IGM-S1, from Novatel, a Raspberry PI portable computer, which records data from both systems and an octopter UAV. The performance of this light-weight system was assessed both for accuracy and with respect to point density, using Ground Control Points (GCP) as reference. Two flights were performed with the UAV octopter carrying the equipment. In the first trial, the flight height was 100 m with six strips over a parking area. The second trial was carried out over an urban park with some buildings and artificial targets serving as reference Ground Control Points. In this experiment a flight height of 70 m was chosen to improve target response. Accuracy was assessed based on control points the coordinates of which were measured in the field. Results showed that vertical accuracy with this prototype is around 30 cm, which is acceptable for forest applications but this accuracy can be improved using further refinements in direct georeferencing and in the system calibration.

scholarly journals THE ASSESSMENT OF CURVED CENTERLINE GENERATION IN HDMAPS BASED ON POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 285-290
Author(s):  
J. C. Zeng ◽  
K. W. Chiang
Keyword(s):  
Laser Scanning ◽  
Road Traffic ◽  
Rapid Development ◽  
Point Clouds ◽  
Horizontal Direction ◽  
Traffic Information ◽  
Large Field ◽  
High Definition ◽  
3D Space ◽  
Lane Line

Abstract. Over the decades, autonomous driving technology has attracted a lot of attention and is under rapid development. However, it still suffers from inadequate accuracy in a certain area, such as the urban area, Global Navigation Satellite System (GNSS) hostile area, due to the multipath interference or Non-Line-of-Sight (NLOS) reception. In order to realize fully autonomous applications, High Definition Maps (HD Maps) become extra assisted information for autonomous vehicles to improve road safety in recent years. Compared with the conventional navigation maps, the accuracy requirement in HD Maps, which is 20 cm in the horizontal direction and 30 cm in 3D space, is considerably higher than the conventional one. Additionally, HD Maps consist of rich and high accurate road traffic information and road elements. For the requirement of high accuracy, conducting a Mobile Laser Scanning (MLS) system is an appropriate method to collect the geospatial data accurately and efficiently. Nowadays, digital vector maps are constructed by digitalizing manually on the collected data. However, the manual process spends a lot of manpower and is not efficient and practical for a large field. Therefore, this paper proposes to automatically construct the crucial road elements, such as road edge, lane line, and centerline, to generate the HD Maps based on point clouds collected by the MMS from the surveying company. The RMSEs in the horizontal direction of the road edge, lane line, and centerline are all lower than 30 cm in 3D space.

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