David Belton and Geoff West CRC for Spatial Information Department of Spatial Sciences Curtin University 1

Outline The aim of this talk is to examine how to automatically align multiple point clouds from Mobile Mapping Systems Examination of the problem Matching between different runs Formulation of the transformation Constraints on the adjustment Improvements still to be made 2

Overview A Mobile Mapping System (MMS) is composed of multiple components including: Positioning system (GNSS) Orientation system (IMU) LiDAR module (SLM) Optional systems such as imagery, odometer, etc These measurements are combined to generate a point cloud 3

0101 System Coordinates of centre of laser footprint on ground in RLG system Coordinates of centre of IMU centre in RLG system Transformation from IMU body system to RLG system Angular misalignment between IMU and laser systems (boresight) Laser range (  ), offset (  ) and encoder angle (  ) Offset between IMU centre and laser centre in IMU system (lever arm) System mount rotation matrix 4

Problem 5

Solution Automatic registration has been researched and implemented in Terrestrial Laser Scanning (TLS) All based on matching elements in the scene (points, primitives, features, objects, etc) and minimising the distance between them The difference is that the transformation in TLS is rigid, where as the transformation in MMS is dynamic (and tied to the trajectory) 6

Iterative Closest Point Method One of the simplest method of registration and alignment. It works by iteratively: Finding the closest point pairs between two datasets. Based on a transformation, finding the parameters that minimises the distance between them, Then repeating the above steps until convergence. Several modifications exist and include: Using the distance along the surface normal Weighting based on distance, local variance, curvature or entropy ICP gives a good final adjustment compared to other methods, but suffers for small convergence region Not a problem with MMS, as it is nominally aligned 7

Rigid Body Adjustment Rigid body adjustment consists of 7 parameters Three rotations ω, φ, and κ Three translations x 0, y 0, and z 0 One scale s (which is fixed in this case) The points in one point cloud are transformed into the coordinate system of the other by the equation: Using ICP, the transformation can be found through least squares by minimising the distance between matching point pairs 8

Dynamic Adjustment A rigid body transformation is unsuitable for MMS, as the adjustment parameters are not constant Instead, the parameters are a function of time (t), or the displacement along the trajectory So either their need to be solved piece wise for different epochs, or then must be solved as a function of time t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t7t7 t8t8 t9t9 t 10 time 9

Dynamic Adjustment t i-1 time titi t i+1 10

Parameters 11

Scary Maths 12

Constraints 13

Curtin University 14

Results A road around Curtin University campus was scanned 3 times with a MDL Dynascan 250. The residuals between points are shown here. Distance between points (m) Before Adjustment After Adjustment 15

Results A profile perpendicular to the direction of the road Before (~4cm) After (~2cm) Before (~8cm) After (~2cm) 16

When someone turns of the base station? 17 Before After

Improvements The points are uniformly weighted Weighting can come from entropy, curvature, distance Can also restrict the adjustment to the dimensionality of the surface At the moment, there is no ground truth Match points to existing infrastructure, boundary outlines, DEM, building models. It uses point to point matching, with is relatively low level Extract features and objects from scene such as walls, posts, vegetation Match features Use object models in the formulation. 18

Thanks 19