Compression of LiDAR Data Using Spatial Clustering and Optimal Plane-Fitting

نویسنده

  • Tarig A. Ali
چکیده

With the advancement in geospatial data acquisition technology, large sizes of digital data are being collected for our world. These include airand space-borne imagery, LiDAR data, sonar data, terrestrial laser-scanning data, etc. LiDAR sensors generate huge datasets of point of multiple returns. Because of its large size, LiDAR data has costly storage and computational requirements. In this article, a LiDAR compression method based on spatial clustering and optimal filtering is presented. The method consists of classification and spatial clustering of the study area image and creation of the optimal planes in the LiDAR dataset through first-order plane-fitting. First-order plane-fitting is equivalent to the Eigen value problem of the covariance matrix. The Eigen value of the covariance matrix represents the spatial variation along the direction of the corresponding eigenvector. The eigenvector of the minimum Eigen value is the estimated normal vector of the surface formed by the LiDAR point and its neighbors. The ratio of the minimum Eigen value and the sum of the Eigen values approximates the change of local curvature, which determines the deviation of the surface formed by a LiDAR point and its neighbors from the tangential plane formed at that neighborhood. If the minimum Eigen value is close to zero for example, then the surface consisting of the point and its neighbors is a plane. The objective of this ongoing research work is basically to develop a LiDAR compression method that can be used in the future at the data acquisition phase to help remove fake returns and redundant points.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Co-registration of Lidar and Photogrammetric Data for Updating Building Databases

Three dimensional data are required for the modeling of urban environments and determining their spatio-temporal changes. The required data are mainly acquired using photogrammetric and lidar collection methods and the data are collected either simultaneously or at different time epochs. In this paper we present the approach and the preliminary results of co-registering these two types of data....

متن کامل

Identification of Structural Defects Using Computer Algorithms

One of the numerous methods recently employed to study the health of structures is the identification of anomaly in data obtained for the condition of the structure, e.g. the frequencies for the structural modes, stress, strain, displacement, speed,  and acceleration) which are obtained and stored by various sensors. The methods of identification applied for anomalies attempt to discover and re...

متن کامل

Planelet Transform: A New Geometrical Wavelet for Compression of Kinect-like Depth Images

With the advent of cheap indoor RGB-D sensors, proper representation of piecewise planar depth images is crucial toward an effective compression method. Although there exist geometrical wavelets for optimal representation of piecewise constant and piecewise linear images (i.e. wedgelets and platelets), an adaptation to piecewise linear fractional functions which correspond to depth variation ov...

متن کامل

ENG 4000: Engineering project ZE Modeller: The LIDAR Point Cloud Processor Preliminary Design Review

Light detection and ranging (LIDAR) instruments collect high density and accurate 3D point clouds of scanned surfaces of objects. For the applications of building reconstruction from ground based LIDAR, the most fundamental spatial information to be extracted are plane features. ZE Modeller implements a point cloud registration and geo-referencing alogithm. Further, ZE Modeller also offers an a...

متن کامل

Think Globally, Cluster Locally: A Unified Framework for Range Segmentation

(a) acquisition (b) clustering (c) refinement Figure 1. Segmentation pipeline. (a) In this simulated LIDAR setup, the frustum represents a scanner projecting a beam onto a 3D model. The beam strikes the nearest surface and measures the distance, rendered here in false color. (b) Similarity based on local plane fitting drives a hierarchical clustering process. (c) Planar components are refined a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013