1 Autonomous Registration of LiDAR Data to Single Aerial Image Takis Kasparis Nicholas S. Shorter University of Central Florida Orlando, Florida, USA Research Website:

2 Objectives – Project Overall Scope 3D Reconstruction from Aerial Imagery and LiDAR data Solely Concentrating on Buildings –Not reconstructing Trees, Cars, Power Lines, Roads, etc. Automate process as much as possible (minimal to no user intervention) End result to appear as 3D Models with images mapped to the models

LIDAR Overview Data Collection –Plane Equipped with GPS, INS & LIDAR –LIDAR – Light Detection and Ranging (active sensor) LiDAR sensor works day or night, cloud coverage or not Collection of 3D points Laser sent out from Emitter, reflects off of Terrain, Returns to Receiver –Receiver measures back scattered electromagnetic radiation (laser intensity) Time Difference Determines Range to Target

Sampling the following scene…

Raw Elevation Plot

Angled, Zoomed In View

Triangulated, Angled, Zoomed In View

Applications for 3D Reconstruction Military Applications –Automatic Target Recognition Commercial –Change Detection (Natural Disasters) –Network Planning for Mobile Communication –Noise Nuisance –Urban Planning

Objectives – Conference Focus Using TIN to upsample LiDAR data Using Psuedo Binning approach to relate interpolated LiDAR (range image) to irregular data points as well as to TIN Unsupervised registration of LiDAR data to aerial imagery

Registration Block Diagram

LiDAR Building Detection Method Hierarchical Triangulated Connected Set Method –Unsupervised Building Detection from LiDAR TIN –No parameter adjustment –Capable of either Labeling Building/Non Building and/or Individual Buildings –Presented at previous conference The Third International Symposium on Communications, Control and Signal Processing (ISCCSP 2008)

Psuedo Binning TIN Up-sampling Extension of Cho et. Al.’s approach to TINs Conceptually overlay grid on top of LiDAR Track Triangles and Raw Points belonging to Interpolated Grid Cells Interpolate Elevation from TIN for Rasterized LiDAR Points Proposed computationally efficient technique for up-sampling

Overlaying Grid Dark Dots – Irregular LiDAR Points Light Dots – Grid Cell Center (interpolated LiDAR point) Dotted line – Triangle Edge Solid line – Grid Cell Border

Definitions for Up-Sampling Procedure M = Number of rows in upsampled image N = Number of columns in upsampled image P = NxM (number of desired grid cell centers) [L x (k), L y (k), L z (k)] = LiDAR Point (3D) [G x (n,m), G y (n,m), G z (n,m)] = Interpolated Point d(k) = distance between Grid Cell G(0,0) and k’th LiDAR point T n,m = triangle which encompasses grid cell center G(n,m)

Up-Sampling Procedure

Up-Sampling Procedure Find Point Closest to G(0,0) – Check Triangles which use L(k) as vertex to see if they encompass G(0,0) Then check triangles adjacent to those –Triangle encompassing G(0,0) dubbed T 0,0 Check to see if T 0,0 encompasses G(0,1) Check to see if triangles adjacent to T 0,0 encompass G(0,1) –Triangle encompassing G(0,1) dubbed T 0,1 Check to see if T 0,1 encompasses G(0,2)

LiDAR Range Image

Registration Impossible to develop registration algorithm for all scenarios, must limit scope Our Application (limiting scope) –Registered images come from two different sources LiDAR Data from LiDAR Sensor up-sampled to Range Image from Proposed Psuedo Binning Approach Aerial Image Captured from Camera on Plane –Registered images assume to only differ via Translation, Rotation, and Scaling

POMF Registration Algorithm Unsupervised Area Based Registration Algorithm –Algorithm operates on image intensity instead of control pts –Because Range Image and Aerial Image of different intensity, preprocessing mandatory –Algorithm capable of automatically registering images differing via following transformations: Scaling from 50 to 200% Rotation Translation (images must significantly overlap)

POMF Preprocessing Scaling –Aerial Image – 15cm pixel spacing –LiDAR Data – 1 pt/1.3m 2 LiDAR data differs in scaling by factor of 10 LiDAR is therefore upsampled and rasterized to range image to approximately same pixel spacing as aerial image Binary Images –Brighter color = building –Darker color = non-building –Two binary images produced One for LiDAR One for Aerial Image

Aerial Binary Image

LiDAR Binary Image

POMF Registration Algorithm takes Log Polar Transform of 2D Discrete Fourier Transform of Both Binary Images –Correlation of phases produces peak at location of rotation and scaling between images Algorithm also takes 2D Discrete Fourier Transforms of Both Binary Images –Correlation of phases produces peak at location corresponding to translations between images

Registration POMF Registration extracts geometric transformation parameters for translation, rotation, scaling Building regions in range image are then translated, rotated, and scaled and thus aligned on top of aerial image

Aerial Image /LiDAR Image

Registered Range to Aerial Image

Future Work Currently working on automatically extracting buildings from aerial image Additional testing on more data sets Improved building detection making use of features from both Aerial and LiDAR

Acknowledgements Funding From Harris Cooperation Fairfield Data Set from Dr. Simone Clode, Dr. Franz Rottensteiner, AAMHatch