Abstract
Abstract. This paper studies self-calibration of a structured light system, which reconstructs 3D information using video from a static consumer camera and a handheld cross line laser projector. Intersections between the individual laser curves and geometric constraints on the relative position of the laser planes are exploited to achieve dense 3D reconstruction. This is possible without any prior knowledge of the movement of the projector. However, inaccurrately extracted laser lines introduce noise in the detected intersection positions and therefore distort the reconstruction result. Furthermore, when scanning objects with specular reflections, such as glossy painted or metalic surfaces, the reflections are often extracted from the camera image as erroneous laser curves. In this paper we investiagte how robust estimates of the parameters of the laser planes can be obtained despite of noisy detections.
Highlights
Triangulation based laser sensors are a popular technique for lowcost rangefinders in mobile robotics (Konolige et al, 2008) and 3D scanning for fabrication (Engelmann, 2011; Winkelbach et al, 2006)
We look at a selfcalibration technique for handheld 3D line laser scanning
We find the offset vectoro that minimizes the sum of the inner product between planes in the set C = {(i, j)|(πi ⊥ πj )} of orthogonal laser planes: We only use a subset of the laser planes to solve for the plane parameters
Summary
Triangulation based laser sensors are a popular technique for lowcost rangefinders in mobile robotics (Konolige et al, 2008) and 3D scanning for fabrication (Engelmann, 2011; Winkelbach et al, 2006). We calibrate the camera to find the standard parameters for modeling the camera intrinsics and we align the two line projectors, such that the laser planes are orthogonal to each other. Finding the intersections of these curves from multiple images and using the orthogonality constraint between all pairs of laser curves that are captured in the same image, yields the parameters of the laser planes. By intersecting these calculated planes with the image rays we create a dense 3D point cloud up to scale. Our reconstruction approach is based on selfcalibration techniques proposed by Furukawa and Kawasaki (2009)
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