Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion

Abstract

Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

“…Regarding a low-cost 3D environment reconstruction system, an SLV sensor is the optimal solution. In contrast to the application of industry measurement, which requires the measured distance to have millimeter accuracy and only works in suitable conditions [32], an SLV sensor for environment reconstruction requires lower accuracy (centimeters) in ranging but needs to deal with challenging conditions such as a varying scanning rate and a change in illumination. Even though an SLV sensor can obtain the depth of a partial image by one measurement, the field of view of the structured light sensor needs to be increased for environment reconstruction.…”