scholarly journals 3D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK

2016 ◽  
Vol XLI-B1 ◽  
pp. 563-569 ◽  
Author(s):  
Y. Feng ◽  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Feature Point ◽  
Sensor Data ◽  
Matching Task ◽  
Lidar Data ◽  
Feature Points ◽  
Range Images ◽  
Back Propagation Algorithm ◽  
Feature Point Extraction

Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.

scholarly journals 3D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK

2016 ◽  
Vol XLI-B1 ◽  
pp. 563-569 ◽  
Author(s):  
Y. Feng ◽  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Feature Point ◽  
Sensor Data ◽  
Matching Task ◽  
Lidar Data ◽  
Feature Points ◽  
Range Images ◽  
Back Propagation Algorithm ◽  
Feature Point Extraction

Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.

Feature Point Extraction in Face Image by Neural Network

2006 ◽  
Author(s):  
Yasuyuki Takahashi ◽  
Stephen Karungaru ◽  
Minoru Fukumi ◽  
Norio Akamatsu
Keyword(s):  
Neural Network ◽  
Face Image ◽  
Feature Point ◽  
Feature Point Extraction

scholarly journals Anisotropic neighborhood searching for point cloud with sharp feature

2020 ◽  
pp. 002029402096424
Author(s):  
Xiaocui Yuan ◽  
Baoling Liu ◽  
Yongli Ma
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accurate Result ◽  
Noise Removal ◽  
Feature Point ◽  
Normal Vector ◽  
Feature Points ◽  
Normal Estimation ◽  
Sharp Feature ◽  
Anisotropic Neighborhood

The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.

A sensor based intelligent system for classification and assistance of diabetes patients in telemedicine technology

2020 ◽  
pp. 1-10
Author(s):  
S, Poonguzhali ◽  
Rekha Chakravarthi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Glucose Level ◽  
Heart Attack ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Wearable Device ◽  
Sensor Data ◽  
Back Propagation Algorithm ◽  
Telemedicine Technology

Diabetes is one of the chronic metabolic disorder. Under diabetic condition, blood glucose level should be properly maintained in order to avoid various major diseases. The condition will be worse when it is not controlled at an earlier stage. Even massive heart attack cannot be identified when the patient has been affected by diabetes. Early diagnosis is required for preventing fatal diseases like cardiac problem, asthma, heart attack etc. In the proposed system measurement of glucose level and Prediction/ diagnosis of diabetes is based on the real time low complexity neural network implemented on a wearable device. A larger network is required for the diagnosis which needs to be present far-off in cloud and initiated for diagnosis and classification process of diabetes whenever it is essential. People can be able to manage and monitor the required basic parameters like heart rate, glucose level, lung condition, pressure of blood using the corresponding light weight biosensors in the wearable device designed through telemedicine technology. The quality of the disease diagnosis and Prediction is improved in this way. Using neural network feed forward prediction model in conjugation with back propagation algorithm and given training data, the system predicts whether the patient is prone to diabetes or not. The proposed work was evaluated using physic sensor data from physio net data base and also tested for real time functioning. The Proposed system found to be efficient in accuracy, sensitivity and fast operative.

scholarly journals A DEEP LEARNING FRAMEWORK FOR ROADS NETWORK DAMAGE ASSESSMENT USING POST-EARTHQUAKE LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 955-961 ◽  
Author(s):  
S. T. Seydi ◽  
H. Rastiveis
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Damage Assessment ◽  
Point Cloud ◽  
High Performance ◽  
Damage Index ◽  
Lidar Data ◽  
The Road ◽  
The Status ◽  
Road Segments

Abstract. Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo a problem. This paper aims to provide and implement a deep learning-based method to determine the status of the streets network after an earthquake using LiDAR point cloud. The proposed framework composes of three main phases: (1) Deep features of LiDAR data are extracted using a Convolutional Neural Network (CNN). (2) The extracted features are used in a multilayer perceptron (MLP) neural network in which debris areas inside the road network are detected. (3) The amount of debris in each road is applied to damage index for classifying the road segments into blocked or un-blocked. To evaluate the efficiency of the proposed framework, LiDAR point cloud of the Port-au-Prince, Haiti after the 2010 Haiti earthquake was used. The overall accuracy of more than 97% proved the high performance of this framework for debris detection. Moreover, analyzing damage assessment of 37 road segments based on the detected debris and comparing to a visually generated damaged map, 31 of the road segments were correctly labelled as either blocked or un-blocked.

scholarly journals LiDAR Odometry by Deep Learning-based Feature Points with Two-step Pose Estimation

2021 ◽  
Author(s):  
Tianyi Liu ◽  
Yan Wang ◽  
xiaoji niu ◽  
Chang Le ◽  
Tisheng Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Field Experiment ◽  
Pose Estimation ◽  
Ground Truth ◽  
Feature Point ◽  
Training Data ◽  
Lidar Data ◽  
Feature Points ◽  
Experiment Data ◽  
Research And Innovation

KITTI dataset is collected from three types of environments, i.e., country, urban and highway The types of feature point cover a variety of scenes. The KITTI dataset provides 22 sequences of LiDAR data. 11 sequences of them from sequence 00 to sequence 10 are "training" data. The training data are provided with ground truth translation and rotation. In addition, field experiment data is collected by low-resolution LiDAR, VLP-16 in Wuhan Research and Innovation Center.

scholarly journals Flexible point cloud matching method based on three-dimensional image feature points

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879503
Author(s):  
Haihua Cui ◽  
Wenhe Liao ◽  
Xiaosheng Cheng ◽  
Ning Dai ◽  
Changye Guo
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Feature Point ◽  
Image Feature ◽  
Surface Measurement ◽  
Surface Model ◽  
Feature Points ◽  
Matching Method ◽  
Dimensional Image

Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point’s nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

scholarly journals Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3058
Author(s):  
Rui Gao ◽  
Jisun Park ◽  
Xiaohang Hu ◽  
Seungjun Yang ◽  
Kyungeun Cho
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Noise Removal ◽  
Sensor Data ◽  
Noise Filtering ◽  
Range Images ◽  
Point Distribution ◽  
Position Sensing ◽  
Sensing Data

Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.

scholarly journals Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256665
Author(s):  
Muhammad Rabani Mohd Romlay ◽  
Azhar Mohd Ibrahim ◽  
Siti Fauziah Toha ◽  
Philippe De Wilde ◽  
Ibrahim Venkat
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Object Recognition ◽  
Extraction Method ◽  
Point Cloud ◽  
Lidar Data ◽  
Low Density ◽  
Feature Extraction Method ◽  
Depth Measurement ◽  
Computing Capacity

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

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