PROBABILISTIC FEATURE MATCHING APPLIED TO PRIMITIVE BASED REGISTRATION OF TLS DATA
- 1SINETICS I2C, EDF R&D, 92140 Clamart, France
- 2The Image Sciences, Computer Sciences and Remote Sensing Laboratory, LSIIT-TRIO UMR 7005, INSA Strasbourg, France
- 3Laboratoire de Physiologie de la Perception et de l'Action, Colège de France, UMR 7152, CNRS, Paris, France
- 4Équipe Géométrie et Dynamique, Institut de Mathématiques de Jussieu, UMR 7586, Paris, France
Keywords: TLS, 3D lines, matching, transformation, probability, industrial installations, indoor geolocation
Abstract. Many industrial applications require dense point clouds of the installations. Acquisition of the rooms, filled with many objects, of an industrial scene leads to many Terrestrial Laser Scanner (TLS) stations. A precise registration of all the per-station point clouds is crucial for the required accuracy of 1-2 cm of final data. Targets and tachometry, current best practice for registration, slows down the survey and limits the number of campaigns. Indoor geolocation system are faster but do not reach the final required accuracy. Otherwise, 3D primitives can be automatically extracted from the dense point clouds and possibly used for registration. In a four step primitive-based registration, Acquisition – Reconstruction – Matching – Solving, the matching is crucial. This article presents a probabilistic test for 3D lines matching using a priori distributions of approximated transformations. The stochastic model of approximated transformations and resulting uncertain lines is introduced. A test is performed on a real dataset of an industrial scene and the results are analysed. Improvements of the presented test and matching framework are also discussed.