Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

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Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽

10.3390/aerospace5030094 ◽

2018 ◽

Vol 5 (3) ◽

pp. 94 ◽

Cited By ~ 5

Author(s):

Hriday Bavle ◽

Jose Sanchez-Lopez ◽

Paloma Puente ◽

Alejandro Rodriguez-Ramos ◽

Carlos Sampedro ◽

...

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Estimation Algorithm ◽

Indoor Environments ◽

3D Point Cloud ◽

Flight Altitude ◽

Cloud Data ◽

3D Point Clouds ◽

Laser Altimeters ◽

Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

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An Improved Method for Restoring the Shape of 3D Point Cloud Surfaces

International Journal of Synthetic Emotions ◽

10.4018/ijse.2018070103 ◽

2018 ◽

Vol 9 (2) ◽

pp. 37-53

Author(s):

Sinh Van Nguyen ◽

Ha Manh Tran ◽

Minh Khai Tran

Keyword(s):

Geometric Modeling ◽

Point Cloud ◽

Point Clouds ◽

Tangent Plane ◽

Surface Patch ◽

3D Point Cloud ◽

Improved Method ◽

Initial Shape ◽

Cloud Data ◽

3D Point Clouds

Building 3D objects or reconstructing their surfaces from 3D point cloud data are researched activities in the field of geometric modeling and computer graphics. In the recent years, they are also studied and used in some fields such as: graph models and simulation; image processing or restoration of digital heritages. This article presents an improved method for restoring the shape of 3D point cloud surfaces. The method is a combination of creating a Bezier surface patch and computing tangent plane of 3D points to fill holes on a surface of 3D point clouds. This method is described as follows: at first, a boundary for each hole on the surface is identified. The holes are then filled by computing Bezier curves of surface patches to find missing points. After that, the holes are refined based on two steps (rough and elaborate) to adjust the inserted points and preserve the local curvature of the holes. The contribution of the proposed method has been shown in processing time and the novelty of combined computation in this method has preserved the initial shape of the surface

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6-DOF Localization for a Mobile Robot Using Outdoor 3D Point Clouds

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2010.p0158 ◽

2010 ◽

Vol 22 (2) ◽

pp. 158-166 ◽

Cited By ~ 7

Author(s):

Taro Suzuki ◽

◽

Yoshiharu Amano ◽

Takumi Hashizume

Keyword(s):

Mobile Robot ◽

Point Cloud ◽

Field Experiments ◽

Point Clouds ◽

Measurement Model ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Continuous Contact ◽

3D Point Clouds

This paper describes outdoor localization for a mobile robot using a laser scanner and three-dimensional (3D) point cloud data. A Mobile Mapping System (MMS) measures outdoor 3D point clouds easily and precisely. The full six-dimensional state of a mobile robot is estimated combining dead reckoning and 3D point cloud data. Two-dimensional (2D) position and orientation are extended to 3D using 3D point clouds assuming that the mobile robot remains in continuous contact with the road surface. Our approach applies a particle filter to correct position error in the laser measurement model in 3D point cloud space. Field experiments were conducted to evaluate the accuracy of our proposal. As the result of the experiment, it was confirmed that a localization precision of 0.2 m (RMS) is possible using our proposal.

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Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100460 ◽

2019 ◽

Vol 8 (10) ◽

pp. 460

Author(s):

Gracchi ◽

Gigli ◽

Noël ◽

Jaboyedoff ◽

Madiai ◽

...

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Wireless Sensor ◽

3D Point Cloud ◽

Cloud Data ◽

3D Point Clouds ◽

Optimal Deployment

In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility.

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POINTO - A LOW COST SOLUTION TO POINT CLOUD PROCESSING

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

10.5194/isprs-archives-xlii-2-w8-111-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 111-117 ◽

Cited By ~ 1

Author(s):

H. Houshiar ◽

S. Winkler

Keyword(s):

User Interfaces ◽

Point Cloud ◽

Low Cost ◽

Point Clouds ◽

3D Point Cloud ◽

Cloud Data ◽

Cloud Processing ◽

3D Point Clouds ◽

Point Cloud Processing ◽

Access To Data

With advance in technology access to data especially 3D point cloud data becomes more and more an everyday task. 3D point clouds are usually captured with very expensive tools such as 3D laser scanners or very time consuming methods such as photogrammetry. Most of the available softwares for 3D point cloud processing are designed for experts and specialists in this field and are usually very large software packages containing variety of methods and tools. This results in softwares that are usually very expensive to acquire and also very difficult to use. Difficulty of use is caused by complicated user interfaces that is required to accommodate a large list of features. The aim of these complex softwares is to provide a powerful tool for a specific group of specialist. However they are not necessary required by the majority of the up coming average users of point clouds. In addition to complexity and high costs of these softwares they generally rely on expensive and modern hardware and only compatible with one specific operating system. Many point cloud customers are not point cloud processing experts or willing to spend the high acquisition costs of these expensive softwares and hardwares. In this paper we introduce a solution for low cost point cloud processing. Our approach is designed to accommodate the needs of the average point cloud user. To reduce the cost and complexity of software our approach focuses on one functionality at a time in contrast with most available softwares and tools that aim to solve as many problems as possible at the same time. Our simple and user oriented design improve the user experience and empower us to optimize our methods for creation of an efficient software. In this paper we introduce Pointo family as a series of connected softwares to provide easy to use tools with simple design for different point cloud processing requirements. PointoVIEWER and PointoCAD are introduced as the first components of the Pointo family to provide a fast and efficient visualization with the ability to add annotation and documentation to the point clouds.

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BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

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

10.5194/isprsarchives-xli-b5-771-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 771-777 ◽

Cited By ~ 1

Author(s):

R. Boerner ◽

M. Kröhnert

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Spatial Information ◽

Point Clouds ◽

Synthetic Image ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Force Matching ◽

3D Point Clouds

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. <br><br> The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

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DETERMINATION OF PARKING SPACE AND ITS CONCURRENT USAGE OVER TIME USING SEMANTICALLY SEGMENTED MOBILE MAPPING DATA

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

10.5194/isprs-archives-xliii-b2-2021-185-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 185-192

Author(s):

A. Leichter ◽

U. Feuerhake ◽

M. Sester

Keyword(s):

Public Space ◽

Point Cloud ◽

Point Clouds ◽

Parking Space ◽

3D Point Cloud ◽

Cloud Data ◽

3D Point Clouds ◽

3D Data ◽

Rgb Images ◽

Bounding Boxes

Abstract. Public space is a scarce good in cities. There are many concurrent usages, which makes an adequate allocation of space both difficult and highly attractive. A lot of space is allocated by parking cars – even if the parking spaces are not occupied by cars all the time. In this work, we analyze space demand and usage by parking cars, in order to evaluate, when this space could be used for other purposes. The analysis is based on 3D point clouds acquired at several times during a day. We propose a processing pipeline to extract car bounding boxes from a given 3D point cloud. For the car extraction we utilize a label transfer technique for transfers from semantically segmented 2D RGB images to 3D point cloud data. This semantically segmented 3D data allows us to identify car instances. Subsequently, we aggregate and analyze information about parking cars. We present an exemplary analysis of the urban area where we extracted 15.000 cars at five different points in time. Based on this aggregated we present analytical results for time dependent parking behavior, parking space availability and utilization.

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BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

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

10.5194/isprs-archives-xli-b5-771-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 771-777

Author(s):

R. Boerner ◽

M. Kröhnert

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Spatial Information ◽

Point Clouds ◽

Synthetic Image ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Force Matching ◽

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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Modeling and Processing of Smart Point Clouds of Cultural Relics with Complex Geometries

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090617 ◽

2021 ◽

Vol 10 (9) ◽

pp. 617

Author(s):

Su Yang ◽

Miaole Hou ◽

Ahmed Shaker ◽

Songnian Li

Keyword(s):

Point Cloud ◽

Semantic Information ◽

Point Clouds ◽

Information Modeling ◽

Complex Geometries ◽

3D Point Cloud ◽

Point Cloud Data ◽

Modeling Framework ◽

Cloud Data ◽

Cultural Relics

The digital documentation of cultural relics plays an important role in archiving, protection, and management. In the field of cultural heritage, three-dimensional (3D) point cloud data is effective at expressing complex geometric structures and geometric details on the surface of cultural relics, but lacks semantic information. To elaborate the geometric information of cultural relics and add meaningful semantic information, we propose a modeling and processing method of smart point clouds of cultural relics with complex geometries. An information modeling framework for complex geometric cultural relics was designed based on the concept of smart point clouds, in which 3D point cloud data are organized through the time dimension and different spatial scales indicating different geometric details. The proposed model allows smart point clouds or a subset to be linked with semantic information or related documents. As such, this novel information modeling framework can be used to describe rich semantic information and high-level details of geometry. The proposed information model not only expresses the complex geometric structure of the cultural relics and the geometric details on the surface, but also has rich semantic information, and can even be associated with documents. A case study of the Dazu Thousand-Hand Bodhisattva Statue, which is characterized by a variety of complex geometries, reveals that our proposed framework is capable of modeling and processing the statue with excellent applicability and expansibility. This work provides insights into the sustainable development of cultural heritage protection globally.

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