Light weighted and Portable LiDAR , VLP-16 Registration 1

12 Recently, Velodyne LiDAR Puck, VLP-16, is introduced in the market. A small, light-weighted LiDAR 13 is welcomed by mobile/UAV mapping communities thanks to its portability and integrality to small 14 aircraft, optical sensors, IMU/GPS etc. While this LiDAR scans 1/3 million points per second in real15 time, its vertical angular resolution, 2° in ±15° vertical field of view greatly limits data acquisition quality 16 compared to other high-end LiDAR. 17 Here, we propose a novel approach for space resection using linear retro-reflective targets to overcome 18 the limitation of large vertical angular resolution. The results show that linear targets are clearly visible 19 and easily extracted. The points on the targets will be line-fitted then, intersections of the lines will play 20 role in space resection for registration. 21 22 INTRODUCTION 23 Space resection, a technique to compute sensor position and its orientation, traditionally requires control 24 points. This process can be considered as a coordinate transformation between sensor coordinate system 25 and reference coordinate system (or global coordinate system) through the same points in two system. 26 Once resection is done, one can place each sensor data to unified coordinate system, in other word, data 27 registration. In this case, one and only condition is that the control point should be visible and accurately 28 measurable in data. It is noted that it is trivial to measure control points in photographs. 29 Recent advancement of Light Detection And Range (LiDAR) technology has reached the light-weighted 30 and portable LiDAR era but at the expense of overall point density in vertical direction. The Velodyne 31 VLP-16, the LiDAR for our study has a vertical angular resolution of 2° in ±15° which makes 16 32 horizontal lines for the interval while horizontal angular resolution is 0.1-0.4°. 33 This large vertical angular resolution limits the usage of control points, because the points may be placed 34 in between lines. Moreover, it is difficult to pin point target’s center, even if points are on the scan line. 35 36 IGTF 2016 Fort Worth, TX, April 11-15, 2016 Submission 149 2 37 Figure 1. Simulated point cloud of VLP-16 when scanned 5x5 meter size room (A) and 38 horizontal/vertical point spacing (B). Horizontal/vertical point spacing are 0.43mm and 8.7 mm in 2.5 39 meter distance respectively. 40 Figure 1 illustrates simulated point cloud of VLP-16. With 0.1° and 2° horizontal and vertical resolution, 41 VLP-16 scans 360° with ±15° vertical field of view, resulting in 57,600 points in 1/5 seconds, or roughly 42 0.3 million points per second (Table 1). 43 In this study, to overcome vertically sparse point distribution, we approach this problem using linear 44 targets (see figure 3B) instead of point targets. Linear targets are made of retro-reflective tapes and their 45 intensities exceed the reflectance of normal surface, which gives easy extraction of targets from point 46 clouds. 47 The extracted linear targets will be converted to points by line-line intersection, followed by RANSAC 48 based line fitting. In the following sections, these steps are explained in detail with experiment results. 49 50 Table 1. The specification of VLP-16 LiDAR 51 Horizontal angel resolution 0.1 to 0.4° degree in 360° Vertical angle resolution 2° in ±15° Rotational speed 5-20 Hz Distance Up to 100 meters Laser class Class 1, eye safe Wavelength 903 nm, near infrared Number of points per second 1/3 million points/second 52 METHODOLOGY 53 Points along the retro-reflective linear targets will generate control points for the resection later. The work 54 flow from targets to control points will be illustrated in figure 3. 55 IGTF 2016 Fort Worth, TX, April 11-15, 2016 Submission 149 3 56 Figure 2. Indoor point cloud sample (A). Targets are visible west side of wall. The photo of retro57 reflective linear targets (B) and enlarged points with targets (C). Color indicates intensity value, 0-100 for 58 normal surface and <100 for targets. 59 60 Figure 3. Work flow from Data acquisition to control point extraction. Once control points are acquired, 61 3D space resection is performed. 62 63 Control Point Extraction and Projection to a Plane 64 As stated earlier, linear target are clearly seen in point cloud (Figure 2) and can easily be separated by its 65 intensity value. Then, those points are projected to best fitted plane. To apply line fitting, we rotate the 66 plane with target points to horizontal plane so that all points lie in 2D space. 67 68 RANSAC Line Extraction and Line-Line Intersection for Control Points 69 RANSAC (Fischler and Bolles 1981), known as outlier-insensitive data fitting algorithm, finds a fitting 70 line and is useful for multiple lines to be detected. For a line detection, RANSAC first starts with 71 randomly selecting two points, which is minimum number for a line construction (AX+BY+C=0). 72 Second, distances between points and a computed line are calculated. With a distance threshold, one can 73 IGTF 2016 Fort Worth, TX, April 11-15, 2016 Submission 149 4 categorize points to inliers and outliers. When this procedure is repeated enough number to be population 74 representable, the batch that has maximum number of inlier point will be a line detected. (Figure 4A) 75 76 Figure 4. Flowchart for RANSAC based line fitting (A) and the scheme of line-line intersection for 77 control point (B) 78 For multiple lines, previous inliers are removed from original points and become new input points. This 79 process can be repeated until all points are used. The targets used in this study are shown in Figure 2B 80 and Figure 4B. We placed targets in a way that we get enough number of intersection points with less 81 linear targets and strong geometry as well. This shape of configuration gives total 5 points with good 82 point distribution. 83 84 3D Space Resection 85 Resection is a technique to position and orient instrument in reference coordinate system (Wolf et al 86 2013). 3D space resection typically contains total 7 parameters – scale, three rotations and three shifts and 87 can be written as follow. 88 [ X Y Z ] = S ∙ [ R11 R12 R13 R21 R22 R23 R31 R32 R33 ] [ x y z ] + [ Tx Ty Tz ] ----(1) 89 Where [X Y Z]’ is reference points, [x y z]’ is LiDAR point in sensor coordinate system, S is scale factor, 90 R11 − R33 are the elements of 3D rotation matrix and [Tx Ty Tz]’ is translation vector. Since LiDAR has 91 metric unit, therefore, one can consider S as 1 or ignore in the equation above. 92 It is non-linear equation and its partial derivatives with respect to 3D coordinate transformation 93 parameters ω, φ, κ, Tx, Ty, Tz, will be written as below. 94 IGTF 2016 Fort Worth, TX, April 11-15, 2016 Submission 149