Edge Detection Using Point Cloud Data for Noncooperative Pose Estimation

2017 ◽  
Vol 54 (2) ◽  
pp. 500-505 ◽  
Author(s):  
Tae W. Lim ◽  
Pierre F. Ramos ◽  
Matthew C. O’Dowd
Keyword(s):  
Edge Detection ◽  
Pose Estimation ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals 3D Hand Pose Estimation in Point Cloud Using 3D Convolutional Neural Network on Egocentric Datasets

2021 ◽  
Vol 2020 (2) ◽  
pp. 87-97
Author(s):  
Lê Văn Hùng
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Color Image ◽  
Point Cloud Data ◽  
Complex Scene ◽  
Cloud Data ◽  
Hand Pose Estimation ◽  
Processing Step ◽  
Egocentric Vision ◽  
Hand Pose

3D hand pose estimation from egocentric vision is an important study in the construction of assistance systems and modeling of robot hand in robotics. In this paper, we propose a complete method for estimating 3D hand posefrom the complex scene data obtained from the egocentric sensor. In which we propose a simple yet highly efficient pre-processing step for hand segmentation. In the estimation process, we used the Hand PointNet (HPN), V2V-PoseNet(V2V), Point-to-Point Regression PointNet (PtoP) for finetuning to estimate the 3D hand pose from the collected data obtained from the egocentric sensor, such as CVRA, FPHA (First-Person Hand Action) datasets. HPN, V2V, PtoP are thedeep networks/Convolutional Neural Networks (CNNs) for estimating 3D hand pose that uses the point cloud data of the hand. We evaluate the estimation results using the preprocessing step and do not use the pre-processing step to see the effectiveness of the proposed method. The results show that 3D distance error is increased many times compared to estimates on the hand datasets are not obstructed (the hand data obtained from surveillance cameras, are viewed from top view, front view, sides view) such as MSRA, NYU, ICVL datasets. The results are quantified, analyzed, shown on the point cloud data of CVAR dataset and projected on the color image of FPHA dataset.

Model-free pose estimation using point cloud data

2019 ◽  
Vol 165 ◽  
pp. 298-311 ◽  
Author(s):  
Tae W. Lim ◽  
Charles E. Oestreich
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Model Free

scholarly journals Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6473
Author(s):  
Tyson Phillips ◽  
Tim D’Adamo ◽  
Peter McAree
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Joint Space ◽  
Object Identification ◽  
Sensor Calibration ◽  
Evidence Based ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Object Pose Estimation ◽  
2D And 3D

The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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