Structured Light Techniques for 3D Surface Reconstruction in Robotic Tasks

Abstract

Robotic tasks such as navigation and path planning can be greatly enhanced by a vision system capable of providing depth perception from fast and accurate 3D surface reconstruction. Focused on robotic welding tasks we present a comparative analysis of a novel mathematical formulation for 3D surface reconstruction and discuss image processing requirements for reliable detection of patterns in the image. Models are presented for a parallel and angled configurations of light source and image sensor. It is shown that the parallel arrangement requires 35% fewer arithmetic operations to compute a point cloud in 3D being thus more appropriate for real-time applications. Experiments show that the technique is appropriate to scan a variety of surfaces and, in particular, the intended metallic parts for robotic welding tasks.

A novel high-frequency encoding algorithm for image compression

“…3D surface reconstruction was performed with our own software developed within the GMPR group [11,12,14]. The justification for introducing 3D reconstruction is that we can make use of a new set of metrics in terms of error measures and perceived quality of the 3D visualization to assess the quality of the compression/ decompression algorithms.…”

“…Bandwidth imposes hard limits on the amount of data transmission and, together with storage costs, calls for more efficient 3D data compression for exchange over the Internet and other networked environments. Using structured light techniques for 3D reconstruction, surface patches can be compressed as a 2D image together with 3D calibration parameters, transmitted over a network and remotely reconstructed (geometry, connectivity and texture map) at the receiving end with the same resolution as the original data [13,14].…”

Robot Trajectory Planning Using OLP and Structured Light 3D Machine Vision

“…The main advantages of the method are speed and accuracy; a surface can be scanned from a single 2D image and processed into 3D in 40ms [14,15]. The expressions to compute the coordinates (x, y, z) of a surface point from a pixel location (v, h) on stripe n (mapping to a point on the surface of the scanned object) is defined as [2,11]:…”

“…The role of the user is limited to high level specification of the welding task and to the confirmation and/or changing of welding parameters and sequences as suggested by the control program. MARWIN uses a 3D structured light scanner where the light source and camera are in a parallel arrangement; its development and mathematical formulation have been described in [2,11].…”

Novel methods of image compression for 3D reconstruction

“…The GMPR 3D reconstruction software creates a 3D representation of the object in a few milliseconds. The main advantages of the developed 3D scannerare speed and accuracy [136]. The quantization values: "0.02", "0.04" and "0.08" in above in Table 3.1 refer to image quality: high, median and low respectively (i.e.…”