Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes.

Mehrdad Eslami,Mohammad Saadatseresht

doi:10.3390/s21010317

Abstract

Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.

Highlights

Developments in recent decades on laser scanners and digital cameras production have affected the productivity of 2D/3D spatial data generation systems
This paper presents a novel method for coarse to fine registration of the photogrammetric imagery and laser scanner point cloud dataset
The method works based on minimizing the distances of the tie object space image points to the laser scanner point cloud data surface

Summary

Introduction

Developments in recent decades on laser scanners and digital cameras production have affected the productivity of 2D/3D spatial data generation systems. The recent development of fast and cheaper computers has influenced the aforementioned data processing. Photogrammetric, computer vision, and remote sensing images are broadly used to generate 2D planimetric maps and land cover classification [1,2], change detection [3], and 3D objects reconstruction [4,5]. Laser scanner point cloud (aerial, Unmanned Aerial Vehicle (UAV), terrestrial, handheld, and mobile) data have largely been used in land cover classification [6], urban building detection and reconstruction [7], and 3D object modelling [8]. There are several challenges with point cloud data in colorizing the points, accurate edges sampling, range accuracy, and the density of the points [9]

Methods

Findings

Discussion

Conclusion