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

Sensors ◽  
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.

scholarly journals Semantic Mapping for Autonomous Subsea Intervention

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6740
Author(s):  
Guillem Vallicrosa ◽  
Khadidja Himri ◽  
Pere Ridao ◽  
Nuno Gracias
Keyword(s):  
Object Recognition ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Bayesian Techniques ◽  
Semantic Map ◽  
3D Point Clouds ◽  
Feature Based ◽  
Object Classes ◽  
High Level

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

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

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.

scholarly journals Scene Wireframes Sketching for Drones

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1193
Author(s):  
Roi Santos ◽  
Xose Pardo ◽  
Xose Fdez-Vidal
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Outdoor Environment ◽  
Point Matching ◽  
3D Point Clouds ◽  
Complementary Approach ◽  
Straight Line ◽  
Scene Representation ◽  
Feature Point Matching ◽  
3D Scene

The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. Likewise, line clouds constructed by short and redundant segments with inaccurate directions limit the understanding of scenes as those that include environments with poor texture, or whose texture resembles a repetitive pattern. The presented approach is based on observation and representation models using the straight line segments, whose resemble the limits of an urban indoor or outdoor environment. The goal of the work is to get a full method based on the matching of lines that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV.

scholarly journals A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

2020 ◽  
Vol 12 (18) ◽  
pp. 2923
Author(s):  
Tengfei Zhou ◽  
Xiaojun Cheng ◽  
Peng Lin ◽  
Zhenlun Wu ◽  
Ensheng Liu
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Real Data ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
General Point ◽  
Exterior Orientation ◽  
Self Calibration ◽  
Laser Scanners ◽  
Orientation Parameters

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

scholarly journals Optimal Prescriptive Trees

2019 ◽  
Vol 1 (2) ◽  
pp. 164-183 ◽  
Author(s):  
Dimitris Bertsimas ◽  
Jack Dunn ◽  
Nishanth Mundru
Keyword(s):  
Decision Making ◽  
Linear Models ◽  
State Of The Art ◽  
Online Advertising ◽  
Real Data ◽  
Attractive Alternative ◽  
High Quality ◽  
And Performance ◽  
Multiple Treatments ◽  
And Personalized Medicine

Motivated by personalized decision making, given observational data [Formula: see text] involving features [Formula: see text], assigned treatments or prescriptions [Formula: see text], and outcomes [Formula: see text], we propose a tree-based algorithm called optimal prescriptive tree (OPT) that uses either constant or linear models in the leaves of the tree to predict the counterfactuals and assign optimal treatments to new samples. We propose an objective function that balances optimality and accuracy. OPTs are interpretable and highly scalable, accommodate multiple treatments, and provide high-quality prescriptions. We report results involving synthetic and real data that show that OPTs either outperform or are comparable with several state-of-the-art methods. Given their combination of interpretability, scalability, generalizability, and performance, OPTs are an attractive alternative for personalized decision making in a variety of areas, such as online advertising and personalized medicine.

Performance and trends in recent opinion retrieval techniques

2013 ◽  
Vol 30 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Sylvester O. Orimaye ◽  
Saadat M. Alhashmi ◽  
Eu-Gene Siew
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Opinion Retrieval ◽  
Future Research Directions ◽  
And Performance ◽  
Comparison Table ◽  
Art Techniques

AbstractThis paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

scholarly journals FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

2016 ◽  
Vol III-3 ◽  
pp. 325-332
Author(s):  
Andreas Kuhn ◽  
Hai Huang ◽  
Martin Drauschke ◽  
Helmut Mayer
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Generation ◽  
Scene Classification ◽  
Semantic Classification ◽  
3D Point Clouds ◽  
Occupancy Grids ◽  
High Scalability

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

scholarly journals INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 311-316 ◽  
Author(s):  
Y. Dehbi ◽  
C. Staat ◽  
L. Mandtler ◽  
L. Pl¨umer
Keyword(s):  
A Priori ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Architectural Patterns ◽  
Grammar Rules ◽  
Probability Densities ◽  
Parse Trees ◽  
Incremental Interpretation ◽  
Context Free ◽  
Building Reconstruction

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

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