3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.

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Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

10.20944/preprints202107.0690.v1 ◽

2021 ◽

Author(s):

Atticus E. L. Stovall ◽

Jeff W Atkins

Keyword(s):

Structural Parameters ◽

Low Cost ◽

Laser Scanner ◽

Point Clouds ◽

Model Reconstruction ◽

Tree Model ◽

Factors Affecting ◽

Laser Scanners ◽

Tree Models ◽

Profile Reconstruction

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

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Development, Calibration and Evaluation of a Portable and Direct Georeferenced Laser Scanning System for Kinematic 3D Mapping

Journal of Applied Geodesy ◽

10.1515/jag-2015-0011 ◽

2015 ◽

Vol 9 (4) ◽

Cited By ~ 4

Author(s):

Erik Heinz ◽

Christian Eling ◽

Markus Wieland ◽

Lasse Klingbeil ◽

Heiner Kuhlmann

Keyword(s):

Laser Scanning ◽

Attitude Determination ◽

Low Cost ◽

Laser Scanner ◽

Point Clouds ◽

Scanning System ◽

Planar Surfaces ◽

Laser Scanners ◽

Scanning Geometry ◽

Laser Scanning System

AbstractIn recent years, kinematic laser scanning has become increasingly popular because it offers many benefits compared to static laser scanning. The advantages include both saving of time in the georeferencing and a more favorable scanning geometry. Often mobile laser scanning systems are installed on wheeled platforms, which may not reach all parts of the object. Hence, there is an interest in the development of portable systems, which remain operational even in inaccessible areas. The development of such a portable laser scanning system is presented in this paper. It consists of a lightweight direct georeferencing unit for the position and attitude determination and a small low-cost 2D laser scanner. This setup provides advantages over existing portable systems that employ heavy and expensive 3D laser scanners in a profiling mode.A special emphasis is placed on the system calibration, i. e. the determination of the transformation between the coordinate frames of the direct georeferencing unit and the 2D laser scanner. To this end, a calibration field is used, which consists of differently orientated georeferenced planar surfaces, leading to estimates for the lever arms and boresight angles with an accuracy of mm and one-tenth of a degree. Finally, point clouds of the mobile laser scanning system are compared with georeferenced point clouds of a high-precision 3D laser scanner. Accordingly, the accuracy of the system is in the order of cm to dm. This is in good agreement with the expected accuracy, which has been derived from the error propagation of previously estimated variance components.

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Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

Remote Sensing ◽

10.3390/rs10111754 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1754 ◽

Cited By ~ 5

Author(s):

Shayan Nikoohemat ◽

Michael Peter ◽

Sander Oude Elberink ◽

George Vosselman

Keyword(s):

3D Modeling ◽

Point Cloud ◽

Essential Role ◽

Low Cost ◽

Laser Scanner ◽

Point Clouds ◽

Semantic Interpretation ◽

Adjacency Graph ◽

Laser Scanners ◽

Indoor Scenes

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes.

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Improved point clouds from a heritage artifact depth low-cost acquisition

Revista Brasileira de Computação Aplicada ◽

10.5335/rbca.v12i1.10019 ◽

2020 ◽

Vol 12 (1) ◽

pp. 84-94

Author(s):

Pedro Oliveira Raimundo ◽

Karl Philips Apaza-Agüero

Keyword(s):

3D Reconstruction ◽

Input Data ◽

Low Cost ◽

Super Resolution ◽

Point Clouds ◽

Insufficient Information ◽

New Approach ◽

Depth Maps ◽

High Level

Poor data acquisition from low-cost cameras, such as low-resolution depth maps or high level of noise or point clouds generated with insufficient information from an object, limits the use of such cameras for heritage artifacts 3D reconstruction. This work proposes to improve this depth low-cost acquisition by using a new approach based on the Super-Resolution technique. The proposed approach has been applied to several artifacts of the Federal University of Bahia Museum of Archaeology and Ethnology (MAE/UFBA). As shown in the results, our approach improved the quality of point clouds generated from tested heritage artifacts. Results indicate that whenever artifact geometry is gained via our method there is actual reconstruction of detail or accuracy improvements, whereas a reduction in number of points of the clouds, if any, would indicate the removal of inconsistencies or noise from the input data without loss of detail.

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LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽

10.3390/s21072263 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2263

Author(s):

Haileleol Tibebu ◽

Jamie Roche ◽

Varuna De Silva ◽

Ahmet Kondoz

Keyword(s):

Laser Scanner ◽

Point Clouds ◽

Light Detection And Ranging ◽

Test Results ◽

New Approach ◽

Occupancy Grid ◽

Laser Scanners ◽

Novel Method ◽

Occupancy Grid Mapping ◽

Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

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Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpz067 ◽

2019 ◽

Vol 93 (3) ◽

pp. 411-429 ◽

Cited By ~ 3

Author(s):

Maria Immacolata Marzulli ◽

Pasi Raumonen ◽

Roberto Greco ◽

Manuela Persia ◽

Patrizia Tartarino

Keyword(s):

Low Cost ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Stem Volume ◽

Forest Plot ◽

3D Point Clouds ◽

Dense Point ◽

Minimum Number ◽

Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

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Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Forests ◽

10.3390/f10070599 ◽

2019 ◽

Vol 10 (7) ◽

pp. 599 ◽

Cited By ~ 4

Author(s):

Ravaglia ◽

Fournier ◽

Bac ◽

Véga ◽

Côté ◽

...

Keyword(s):

Laser Scanner ◽

Point Clouds ◽

Stem Diameter ◽

Trunk Diameter ◽

Diameter At Breast Height ◽

Breast Height ◽

Forest Inventories ◽

Laser Scanners ◽

Step Algorithm ◽

Coniferous Plantations

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.

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EXPLOITING INDOOR MOBILE LASER SCANNER TRAJECTORIES FOR SEMANTIC INTERPRETATION OF POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-355-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 355-362 ◽

Cited By ~ 15

Author(s):

S. Nikoohemat ◽

M. Peter ◽

S. Oude Elberink ◽

G. Vosselman

Keyword(s):

Data Collection ◽

Feature Detection ◽

Laser Scanner ◽

Point Clouds ◽

Semantic Interpretation ◽

Indoor Environments ◽

Valuable Source ◽

Laser Scanners ◽

Occlusion Reasoning

The use of Indoor Mobile Laser Scanners (IMLS) for data collection in indoor environments has been increasing in the recent years. These systems, unlike Terrestrial Laser Scanners (TLS), collect data along a trajectory instead of at discrete scanner positions. In this research, we propose several methods to exploit the trajectories of IMLS systems for the interpretation of point clouds. By means of occlusion reasoning and use of trajectory as a set of scanner positions, we are capable of detecting openings in cluttered indoor environments. In order to provide information about both the partitioning of the space and the navigable space, we use the voxel concept for point clouds. Furthermore, to reconstruct walls, floor and ceiling we exploit the indoor topology and plane primitives. The results show that the trajectory is a valuable source of data for feature detection and understanding of indoor MLS point clouds.

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PRECISE INDOOR LOCALIZATION FOR MOBILE LASER SCANNER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-4-w5-1-2015 ◽

2015 ◽

Vol XL-4/W5 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

R. Kaijaluoto ◽

A. Hyyppä

Keyword(s):

Data Collection ◽

Point Cloud ◽

Indoor Localization ◽

Collection Efficiency ◽

Laser Scanner ◽

Functional Method ◽

Single View ◽

Laser Scanners ◽

Point Data ◽

High Level

Accurate 3D data is of high importance for indoor modeling for various applications in construction, engineering and cultural heritage documentation. For the lack of GNSS signals hampers use of kinematic platforms indoors, TLS is currently the most accurate and precise method for collecting such a data. Due to its static single view point data collection, excessive time and data redundancy are needed for integrity and coverage of data. However, localization methods with affordable scanners are used for solving mobile platform pose problem. The aim of this study was to investigate what level of trajectory accuracies can be achieved with high quality sensors and freely available state of the art planar SLAM algorithms, and how well this trajectory translates to a point cloud collected with a secondary scanner. In this study high precision laser scanners were used with a novel way to combine the strengths of two SLAM algorithms into functional method for precise localization. We collected five datasets using Slammer platform with two laser scanners, and processed them with altogether 20 different parameter sets. The results were validated against TLS reference. The results show increasing scan frequency improves the trajectory, reaching 20 mm RMSE levels for the best performing parameter sets. Further analysis of the 3D point cloud showed good agreement with TLS reference with 17 mm positional RMSE. With precision scanners the obtained point cloud allows for high level of detail data for indoor modeling with accuracies close to TLS at best with vastly improved data collection efficiency.

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ASSESSING THE ACCURACY AND PRECISION OF IMPERFECT POINT CLOUDS FOR 3D INDOOR MAPPING AND MODELING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w6-3-2018 ◽

2018 ◽

Vol IV-4/W6 ◽

pp. 3-10

Author(s):

J. Chen ◽

O. E. Mora ◽

K. C. Clarke

Keyword(s):

Low Cost ◽

Random Noise ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Accuracy And Precision ◽

Wide Range ◽

Sharp Edges ◽

The Cost ◽

Indoor Spaces

Abstract. In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.

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