Data Registration with Ground Points for Roadside LiDAR Sensors
نویسندگان
چکیده
منابع مشابه
Automatic registration method for mobile LiDAR data
We present an automatic mutual information (MI) registration method for mobile LiDAR and panoramas collected from a driving vehicle. The suitability of MI for registration of aerial LiDAR and aerial oblique images has been demonstrated under an assumption that minimization of joint entropy (JE) is a sufficient approximation of maximization of MI. We show that this assumption is invalid for the ...
متن کاملImprovement of Lidar Data Accuracy Using Lidar-Specific Ground Targets
With recent advances of lidar technology, the accuracy potential of lidar data has significantly improved. State-ofthe-art lidar systems can achieve 2 to 3 cm ranging accuracy under ideal conditions, which is the accuracy level required by engineering scale mapping. However, this is also the accuracy range that cannot be realized by routine navigation-based direct sensor platform orientation. F...
متن کاملAutomated Searching of Ground Points from Airborne Lidar Data Using a Climbing and Sliding Method
The extraction of a digital elevation model (DEM) from airborne lidar point clouds is an important task in the field of geoinformatics. In this paper, we describe a new automated scheme that utilizes the so-called “climbingand-sliding” method to search for ground points from lidar point clouds for DEM generation. The new method has the capability of performing a local search while preserving th...
متن کاملMachine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملCNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2019
ISSN: 2072-4292
DOI: 10.3390/rs11111354