scholarly journals Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 428 ◽  
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Juntao Xiong ◽  
Jinhui Li
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Detection Method ◽  
Low Cost ◽  
Three Dimensional ◽  
Estimation Method ◽  
Depth Image ◽  
Convolutional Network ◽  
Guava Fruit ◽  
Center Position

Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot.

scholarly journals GEOREFERENCING AND REPROJECTION ERROR INVESTIGATION ON IMAGE BASED 3D DIGITIZATION AND MAPPING OF HISTORICAL BUILDINGS

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 71-75
Author(s):  
C. Altuntas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Control Points ◽  
Historical Buildings ◽  
Reprojection Error ◽  
Dense Point ◽  
Point Cloud Model

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

2013 ◽  
Vol 760-762 ◽  
pp. 1556-1561
Author(s):  
Ting Wei Du ◽  
Bo Liu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Depth Image ◽  
Cloud Data ◽  
Mixture Model Clustering ◽  
Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

scholarly journals Pose Estimation of Sweet Pepper through Symmetry Axis Detection

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3083 ◽  
Author(s):  
Hao Li ◽  
Qibing Zhu ◽  
Min Huang ◽  
Ya Guo ◽  
Jianwei Qin
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Fruits And Vegetables ◽  
Symmetry Axis ◽  
Symmetry Plane ◽  
Estimation Method ◽  
Sweet Pepper ◽  
Average Error ◽  
Simulated Test ◽  
Local Plane

The space pose of fruits is necessary for accurate detachment in automatic harvesting. This study presents a novel pose estimation method for sweet pepper detachment. In this method, the normal to the local plane at each point in the sweet-pepper point cloud was first calculated. The point cloud was separated by a number of candidate planes, and the scores of each plane were then separately calculated using the scoring strategy. The plane with the lowest score was selected as the symmetry plane of the point cloud. The symmetry axis could be finally calculated from the selected symmetry plane, and the pose of sweet pepper in the space was obtained using the symmetry axis. The performance of the proposed method was evaluated by simulated and sweet-pepper cloud dataset tests. In the simulated test, the average angle error between the calculated symmetry and real axes was approximately 6.5°. In the sweet-pepper cloud dataset test, the average error was approximately 7.4° when the peduncle was removed. When the peduncle of sweet pepper was complete, the average error was approximately 6.9°. These results suggested that the proposed method was suitable for pose estimation of sweet peppers and could be adjusted for use with other fruits and vegetables.

scholarly journals Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation

2020 ◽  
Vol 10 (16) ◽  
pp. 5442
Author(s):  
Ryo Hachiuma ◽  
Hideo Saito
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Shape Representation ◽  
Estimation Method ◽  
Depth Image ◽  
Six Degrees Of Freedom ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Primitive Shape ◽  
Conventional Methods

This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.

Replica Fabrication of a Greek Paleontological Find Utilising Laser Scanning and Fused Deposition Modeling

2014 ◽  
Vol 657 ◽  
pp. 795-799 ◽  
Author(s):  
Anastasios Chatzikonstantinou ◽  
Dimitrios Tzetzis ◽  
Panagiotis Kyratsis ◽  
Nikolaos Bilalis
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Fused Deposition Modeling ◽  
Complex Geometry ◽  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Analytical Methodology ◽  
Fused Deposition ◽  
Deposition Modeling

The current work demonstrates a feasibility study on the generation of a copy, having a highly complex geometry, of a Greek paleontological find utilising reverse engineering and low-cost rapid prototyping techniques. A part of the jaw bone of a cave bear (Ursus spelaeus) that lived during the Pleistocene and became extinct about 10,000 years ago was digitized using a three-dimensional laser scanner. The resulting point-cloud of the scans was treated with a series of advanced software for the creation of surfaces and ultimately for a digital model. The generated model was three-dimensionally built by the aid of a Fused Deposition Modeling (FDM) apparatus. An analytical methodology is presented revealing the step by step approach from the scanning to the prototyping. It is believed that a variety of interested parties could benefit from such an analytical approach, including, production engineers, three-dimensional CAD users and designers, paleontologists and museum curators.

A new pose estimation method for non-cooperative spacecraft based on point cloud

2019 ◽  
Vol 12 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Zhiming Chen ◽  
Lei Li ◽  
Yunhua Wu ◽  
Bing Hua ◽  
Kang Niu
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Laser Scanning ◽  
Rapid Development ◽  
Estimation Method ◽  
Life Extension ◽  
Laser Radar ◽  
Quality Of Data ◽  
Proximity Space ◽  
Content Type

Purpose On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues. Design/methodology/approach In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation. Findings The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation. Research limitations/implications Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range. Practical implications The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked. Originality/value This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

scholarly journals Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5428 ◽  
Author(s):  
Yibin Wu ◽  
Xiaoji Niu ◽  
Junwei Du ◽  
Le Chang ◽  
Hailiang Tang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Field Experiments ◽  
Low Cost ◽  
Estimation Method ◽  
Measurement Unit ◽  
Vertical Position ◽  
Autonomous Operation ◽  
Micro Electromechanical System ◽  
Mems Imu

The fully autonomous operation of multirotor unmanned air vehicles (UAVs) in many applications requires support of precision landing. Onboard camera and fiducial marker have been widely used for this critical phase due to its low cost and high effectiveness. This paper proposes a six-degrees-of-freedom (DoF) pose estimation solution for UAV landing based on an artificial marker and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). The position and orientation of the landing maker are measured in advance. The absolute position and heading of the UAV are estimated by detecting the marker and extracting corner points with the onboard monocular camera. To achieve continuous and reliable positioning when the marker is occasionally shadowed, IMU data is fused by an extended Kalman filter (EKF). The error terms of the IMU sensor are modeled and estimated. Field experiments show that the positioning accuracy of the proposed system is at centimeter level, and the heading error is less than 0.1 degrees. Comparing to the marker-based approach, the roll and pitch angle errors decreased by 33% and 54% on average. Within five seconds of vision outage, the average drifts of the horizontal and vertical position were 0.41 and 0.09 m, respectively.

scholarly journals Human pose estimation method based on single depth image

2018 ◽  
Vol 12 (6) ◽  
pp. 919-924 ◽  
Author(s):  
Qingqiang Wu ◽  
Guanghua Xu ◽  
Min Li ◽  
Longting Chen ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Depth Image ◽  
Human Pose Estimation ◽  
Human Pose
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