scholarly journals Pengembangan Algoritma Pereduksi Noise Pada Point Cloud Data LiDAR Dua Dimensi Untuk Aplikasi Kendaraan Listrik Otonom Sederhana

2021 ◽  
Vol 3 ◽  
pp. 145-156
Author(s):  
Mas’ud Abdur Rosyid ◽  
Yusuf Suhaimi Daulay ◽  
Denden Mohamad Arifin ◽  
Ardian Infantono ◽  
Arief Suryadi Satyawan ◽  
...  
Keyword(s):  
Point Cloud ◽  
Raspberry Pi ◽  
Point Cloud Data ◽  
Light Detection ◽  
Cloud Data

Penerapan teknologi LiDAR 2 dimensi (Light Detection And Rangging)  terkadang terkendala oleh adanya anomaly data atau noise sehingga mempengaruhi keakuratan dalam mendeteksi objek yang sesungguhnya. Jika tidak diatasi dengan baik maka dapat menggangu operasional kerjanya, terlebih lagi jika diterapkan pada kendaraan listrik otonom. Oleh sebab itu perlu upaya untuk mereduksi noise yang diimplementasikan pada software pemroses data LiDAR. Pada penelitian ini dilakukan pengembangan teknologi pereduksi noise yang muncul  pada point cloud data LiDAR dua dimensi. Adapun konsep yang diterapkan adalah pengembangan algoritma pengolahan data LiDAR secara sistematis. Desain algoritma ini berisikan visualiasi dari pendeteksian objek, penyimpanan point cloud data LiDAR sebagai informasi objek yang terdeteksi, serta metode pengurangan  noise pada point cloud data LiDAR dua dimensi tersebut. Algoritma ini di realisasikan dalam bentuk software pada perangkat keras Raspberry Pi 4, dengan menggunakan bahasa pemrograman Python. Terdapat enam Algoritma yang digunakan untuk mereduksi atau menghilangkan noise, yaitu Algoritma 1, Algoritma 2, Algoritma 3, Algoritma 4, Algoritma 5, Algoritma 6. Hasil percobaan memperlihatkan bahwa dari keenam Algoritma yang di buat mampu menampilkan visualisasi data berdasarkan sistem pemetaan 2 dimensi yang terkoreksi dari noise. Keenam Algoritma tersebut berhasil menyeleksi noise hingga 100%, meskipun kurang lebih 80% data yang dianggap benar tidak dapat disajikan. Meskipun hanya dengan 20% data benar, namun struktur objek masih dapat dikenali.

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

2020 ◽  
Vol 2674 (11) ◽  
pp. 192-206
Author(s):  
Yaneev Golombek ◽  
Wesley E. Marshall
Keyword(s):  
Point Cloud ◽  
High Density ◽  
Light Detection And Ranging ◽  
Quality Level ◽  
United States Geological Survey ◽  
Point Cloud Data ◽  
Street Trees ◽  
Light Detection ◽  
Cloud Data ◽  
Traffic Lights

This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.

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.

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Building Construction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Modeling Approach
Sign in / Sign up

Export Citation Format

Share Document