Semantic Mapping for Autonomous Subsea Intervention

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎𝜃) and vertical (𝜎𝛼) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎𝜃=±36.6𝑐𝑐 and 𝜎𝛼=±17.8𝑐𝑐, respectively. On the other hand, a priori precision of the range observation (𝜎𝜌) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎𝜌=±2−12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

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AN EMPRICAL POINT ERROR MODEL FOR TLS DERIVED POINT CLOUDS

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

10.5194/isprs-archives-xli-b5-557-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 557-563

Author(s):

Mustafa Ozendi ◽

Devrim Akca ◽

Hüseyin Topan

Keyword(s):

Real World ◽

Incidence Angle ◽

A Priori ◽

Laser Scanner ◽

Point Clouds ◽

Error Model ◽

Random Errors ◽

Data Set ◽

3D Point Clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎𝜃) and vertical (𝜎𝛼) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎𝜃=±36.6𝑐𝑐 and 𝜎𝛼=±17.8𝑐𝑐, respectively. On the other hand, a priori precision of the range observation (𝜎𝜌) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎𝜌=±2−12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

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3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Sensors ◽

10.3390/s19204451 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4451

Author(s):

Himri ◽

Ridao ◽

Gracias

Keyword(s):

Object Recognition ◽

State Of The Art ◽

A Priori ◽

Real Data ◽

Point Clouds ◽

Performance Comparison ◽

Research Centre ◽

Laser Sensor ◽

3D Point Clouds ◽

And Performance

This paper addresses the problem of object recognition from colorless 3D point clouds inunderwater environments. It presents a performance comparison of state-of-the-art global descriptors,which are readily available as open source code. The studied methods are intended to assistAutonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwaterInspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as beingrepresentative of IMR applications whose shape is typically known a priori. As such, CAD modelswere used to create virtual views of the objects under realistic conditions of added noise and varyingresolution. Extensive experiments were conducted from both virtual scans and from real data collectedwith an AUV equipped with a fast laser sensor developed in our research centre. The underwatertesting was conducted from a moving platform, which can create deformations in the perceived shapeof the objects. These effects are considerably more difficult to correct than in above-water counterparts,and therefore may affect the performance of the descriptor. Among other conclusions, the testing weconducted illustrated the importance of matching the resolution of the database scans and test scans,as this significantly impacted the performance of all descriptors except one. This paper contributes tothe state-of-the-art as being the first work on the comparison and performance evaluation of methodsfor underwater object recognition. It is also the first effort using comparison of methods for dataacquired with a free floating underwater platform.

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Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

Engineering Proceedings ◽

10.3390/engproc2021005059 ◽

2021 ◽

Vol 5 (1) ◽

pp. 59

Author(s):

Gaël Kermarrec ◽

Niklas Schild ◽

Jan Hartmann

Keyword(s):

Least Squares ◽

Laser Scanner ◽

Real Data ◽

Point Clouds ◽

Statistical Testing ◽

Normal Vector ◽

Engineering Structures ◽

3D Point Clouds ◽

Least Squares Adjustment ◽

Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

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Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽

10.3390/s21010201 ◽

2020 ◽

Vol 21 (1) ◽

pp. 201

Author(s):

Michael Bekele Maru ◽

Donghwan Lee ◽

Kassahun Demissie Tola ◽

Seunghee Park

Keyword(s):

Optical Sensors ◽

Point Cloud ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Depth Camera ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

3D Point Clouds ◽

3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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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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SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS

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

10.5194/isprs-archives-xlii-2-w7-971-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 971-976 ◽

Cited By ~ 2

Author(s):

W. Yao ◽

P. Polewski ◽

P. Krzystek

Keyword(s):

Semantic Analysis ◽

Sampling Rate ◽

Point Clouds ◽

Shape Priors ◽

High Point ◽

Urban Road ◽

3D Point Clouds ◽

Point Density ◽

Object Classes ◽

Road Corridors

In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.

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Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Remote Sensing ◽

10.3390/rs12111870 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1870 ◽

Cited By ~ 3

Author(s):

Qingqing Li ◽

Paavo Nevalainen ◽

Jorge Peña Queralta ◽

Jukka Heikkonen ◽

Tomi Westerlund

Keyword(s):

Autonomous Robots ◽

Laser Scanner ◽

Point Clouds ◽

Time Data ◽

Unstructured Environments ◽

Accurate Localization ◽

Novel Approach ◽

Local Point ◽

Feature Based ◽

Real Time Data Processing

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.

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