IGTF 2016 Fort Worth, TX, April 11-15, 2016 Submission 149

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1 IGTF 26 Fort Worth, TX, April -5, Light weighted and Portable LiDAR, VLP-6 Registration Yushin Ahn (yahn@mtu.edu), Kyung In Huh (khuh@cpp.edu), Sudhagar Nagarajan (snagarjan@fau.edu), Jin Hong (hongjs@etsu.edu),ki In Bang(kiinbang@yorku.ca) Surveying Engineering, Integrated Geospatial technology MS, Michigan Technological University, Houghton, MI Department of Geography and Anthropology, California State Polytechnic University in Pomona, CA Engineering Technology, Surveying & Digital Media, East Tennessee State University, TN Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, FL Earth & Space Science & Engineering, York University, Toronto, CA ABSTRACT Recently, Velodyne LiDAR Puck, VLP-6, is introduced in the market. A small, light-weighted LiDAR is welcomed by mobile/uav mapping communities thanks to its portability and integrality to small aircraft, optical sensors, IMU/GPS etc. While this LiDAR scans /3 million points per second in realtime, its vertical angular resolution, 2 in ±5 vertical field of view greatly limits data acquisition quality compared to other high-end LiDAR. Here, we propose a novel approach for space resection using linear retro-reflective targets to overcome the limitation of large vertical angular resolution. The results show that linear targets are clearly visible and easily extracted. The points on the targets will be line-fitted then, intersections of the lines will play role in space resection for registration INTRODUCTION Space resection, a technique to compute sensor position and its orientation, traditionally requires control points. This process can be considered as a coordinate transformation between sensor coordinate system and reference coordinate system (or global coordinate system) through the same points in two system. Once resection is done, one can place each sensor data to unified coordinate system, in other word, data registration. In this case, one and only condition is that the control point should be visible and accurately measurable in data. It is noted that it is trivial to measure control points in photographs. Recent advancement of Light Detection And Range (LiDAR) technology has reached the light-weighted and portable LiDAR era but at the expense of overall point density in vertical direction. The Velodyne VLP-6, the LiDAR for our study has a vertical angular resolution of 2 in ±5 which makes 6 horizontal lines for the interval while horizontal angular resolution is.-.4. This large vertical angular resolution limits the usage of control points, because the points may be placed in between lines. Moreover, it is difficult to pin point target s center, even if points are on the scan line. 36

2 IGTF 26 Fort Worth, TX, April -5, Figure. Simulated point cloud of VLP-6 when scanned 5x5 meter size room (A) and horizontal/vertical point spacing (B). Horizontal/vertical point spacing are.43mm and 8.7 mm in 2.5 meter distance respectively. Figure illustrates simulated point cloud of VLP-6. With. and 2 horizontal and vertical resolution, VLP-6 scans 36 with ±5 vertical field of view, resulting in 57,6 points in /5 seconds, or roughly.3 million points per second (Table ). In this study, to overcome vertically sparse point distribution, we approach this problem using linear targets (see figure 3B) instead of point targets. Linear targets are made of retro-reflective tapes and their intensities exceed the reflectance of normal surface, which gives easy extraction of targets from point clouds. The extracted linear targets will be converted to points by line-line intersection, followed by RANSAC based line fitting. In the following sections, these steps are explained in detail with experiment results Table. The specification of VLP-6 LiDAR Horizontal angel resolution. to.4 degree in 36 Vertical angle resolution 2 in ±5 Rotational speed 5-2 Hz Distance Up to meters Laser class Class, eye safe Wavelength 93 nm, near infrared Number of points per second /3 million points/second METHODOLOGY Points along the retro-reflective linear targets will generate control points for the resection later. The work flow from targets to control points will be illustrated in figure 3. 2

3 IGTF 26 Fort Worth, TX, April -5, Figure 2. Indoor point cloud sample (A). Targets are visible west side of wall. The photo of retroreflective linear targets (B) and enlarged points with targets (C). Color indicates intensity value, - for normal surface and < for targets Figure 3. Work flow from Data acquisition to control point extraction. Once control points are acquired, 3D space resection is performed Control Point Extraction and Projection to a Plane As stated earlier, linear target are clearly seen in point cloud (Figure 2) and can easily be separated by its intensity value. Then, those points are projected to best fitted plane. To apply line fitting, we rotate the plane with target points to horizontal plane so that all points lie in 2D space RANSAC Line Extraction and Line-Line Intersection for Control Points RANSAC (Fischler and Bolles 98), known as outlier-insensitive data fitting algorithm, finds a fitting line and is useful for multiple lines to be detected. For a line detection, RANSAC first starts with randomly selecting two points, which is minimum number for a line construction (AX+BY+C=). Second, distances between points and a computed line are calculated. With a distance threshold, one can 3

4 IGTF 26 Fort Worth, TX, April -5, categorize points to inliers and outliers. When this procedure is repeated enough number to be population representable, the batch that has maximum number of inlier point will be a line detected. (Figure 4A) Figure 4. Flowchart for RANSAC based line fitting (A) and the scheme of line-line intersection for control point (B) For multiple lines, previous inliers are removed from original points and become new input points. This process can be repeated until all points are used. The targets used in this study are shown in Figure 2B and Figure 4B. We placed targets in a way that we get enough number of intersection points with less linear targets and strong geometry as well. This shape of configuration gives total 5 points with good point distribution D Space Resection Resection is a technique to position and orient instrument in reference coordinate system (Wolf et al 23). 3D space resection typically contains total 7 parameters scale, three rotations and three shifts and can be written as follow. X R R 2 R 3 x Tx [ Y] = S [ R 2 R 22 R 23 ] [ y] + [ Ty] () Z R 3 R 32 R 33 z Tz Where [X Y Z] is reference points, [x y z] is LiDAR point in sensor coordinate system, S is scale factor, R R 33 are the elements of 3D rotation matrix and [Tx Ty Tz] is translation vector. Since LiDAR has metric unit, therefore, one can consider S as or ignore in the equation above. It is non-linear equation and its partial derivatives with respect to 3D coordinate transformation parameters - ω, φ, κ, Tx, Ty, Tz, will be written as below. 4

5 IGTF 26 Fort Worth, TX, April -5, X X Y Y Z Z X Y w Z w X Y Z X Y Z d d d dtx dty dtz (2) Where [X Y Z ] is computed value with initial approximation. It has B=AX matrix form and using least squares method, solution can be found by solving X = (A T A) A T B and updating parameters in an iterative fashion. 99 EXPERIMENT and RESULTS We used total 4 set of scans and points on the retro-reflective targets were successfully extracted Figure 5. Four scans used in the study - reference scan (A), rotated about X axis (B), clockwise rotated about Z axis (C) and anti-clockwise rotated about Z axis (D) Figure 6. Extracted control points from each scan. 4 lines can be extracted and 5 intersections are stored as control points. 5

6 IGTF 26 Fort Worth, TX, April -5, Using control points followed by line fitting and line-line intersection, space resection is performed and the reference standard deviations (σ ) for resection adjustment are.224,.45 and.24 meter for set B, C and D respectively Figure 7. Raw data (A), result of registration (B) and refinement using Iterative closest point (C) (Ramer and Bolles 98) Resection results, scanner position and orientation, can be used to transform all the points to the reference coordinate through equation (). Figure 7B shows all four scans together and one can see control points derived from linear target align each scan well. When corners are compared, the mean and standard deviation are.59 and.28 respectively. One can perform ICP based registration for refinement, since resection provides good approximation. Then the mean and standard deviation reduced to.33 and.6 respectively. CONCLUSION We showed retro-reflective linear targets can be clearly visible in point cloud and four target lines and 5 intersection were successfully extracted. Through the step, we also showed the potential of linear target derived points in space resection procedure. The proposed method will effectively connect real world coordinate system when linear targets are measured using surveying equipment, such as total station. Furthermore, when portable LiDAR is used indoors, conventional registration using sole point clouds, such as ICP may fail, mainly due to the fact that each horizontal scan is similar each other in vertical direction. REFERENCES Fischler, M., and R. Bolles, (98). Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography, Commun. Assoc. Comp. 738 Mach., 24: Ramer, U., (972) "An iterative procedure for the polygonal approximation of plane curves," Computer Graphics and Image Processing, (3), (972) Wolf, P. R., B.A. Dewitt and B. Wilkinson, (23). Elements of photogrammetry with 97 applications in GIS, McGraw Hill, 4th Edition, 23, 676pp. 6