3D registration based on the direction sensor measurements. (April 2019)

• Record Type: Journal Article
• Authors: Pribanić, Tomislav; Petković, Tomislav; Đonlić, Matea
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 532
• Page End: 546
• Publication Date: 2019-04
• Subjects: 3D rigid registration -- 3D reconstruction -- Smartphone -- Tablet -- Accelerometer -- Magnetometer -- Structured light pattern; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.12.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Time efficient decoupling of 3D registration problem into rotation and translation. 3D registration using only one direction sensor, e.g. accelerometer or magnetometer. Reduction of 6 DOF registration problem to 4 DOF problem using a direction sensor. No need to collect/track 3D data between changes of viewpoints (less processing). Demonstration of 3D structured light scanner on a mobile platform. Abstract: 3D registration is a very active topic, spanning research areas such as computational geometry, computer graphics and pattern recognition. It aims to solve spatial transformation that aligns two point clouds. In this work we propose the use of a single direction sensor, such as an accelerometer or a magnetometer, commonly available on contemporary mobile platforms, such as tablets and smartphones. Both sensors have been heavily investigated earlier, but only for joint use with other sensors, such as gyroscopes and GPS. We show a time-efficient and accurate 3D registration method that takes advantage of only either an accelerometer or a magnetometer. We demonstrate a 3D reconstruction of individual point clouds and the proposed 3D registration method on a tablet equipped with an accelerometer or a magnetometer. However, we point out that the proposed method is not restricted to mobile platforms. Indeed, it can easily be applied in any 3D measurement system that is upgradable with some ubiquitous direction sensor, for example by adding a smartphone equipped with either an accelerometer or a magnetometer. We compare the proposed method against several state-of-the-art methods implemented in the open source Point Cloud Library (PCL). The proposed method outperforms the PCL methods tested, both in terms of processing time and accuracy.