<title>Comparison of pose estimation methods of a 3D laser tracking system using triangulation and photogrammetry techniques</title>

Abstract

In this paper, we compare the accuracy and resolution of a 3D-laser scanner prototype that tracks in real-time and computes the relative pose of objects in a 3D space. This 3D-laser scanner prototype was specifically developed to study the use of such a sensor for space applications. The main objective of this project is to provide a robust sensor to assist in the assembly of the International Space Station where high tolerance to ambient illumination is paramount. The laser scanner uses triangulation based range data and photogrammetry methods (spatial resection), to calculate the relative pose of objects. Range information is used to increase the accuracy of the sensing system and to remove erroneous measurements. Two high-speed galvanometers and a collimated laser beam address individual targets mounted on an object to a resolution corresponding to an equivalent imager of 10000×10000 pixels. Knowing the position coordinates of predefined targets on the objects, their relative poses can be computed using either the scanner calibrated 3D coordinates or spatial resection methods.

Vision-aided inertial navigation system for robotic mobile mapping

“…More recently, Hassan et al (2006) present a very encouraging simulation study that considers data integration between GPS/INS and an array of fixed cameras with all sensors placed on the roof of a surveying van. Other studies have used photogrammetric localisation in industry, but these were limited in using one stationary camera to localise moving objects (ElHakim et al 1997, Blais et al 2001, Bö hm et al 2001. Hofmann-Wellenhof et al (2003) describe briefly the use of photogrammetry as a navigation method, but no further discussion is held.…”

An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

“…This is the strategy pursued by the iterative closest point (ICP) method (Besl & McKay, ), and many have pursued using similar metrics. Blais, Beraldin, El‐Hakim, and Cournoyer () minimize the quadratic error vector of space station geometry features and three‐dimensional (3D) LiDAR point‐clouds, and similarly Chiu, Tsai, and Chang () minimize a point to model objective function for a random bin picking robot. Nair and Cavallaro () perform face detection by minimizing the deviation of anthropometric facial features from 3D face models.…”