Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Tomi Rosnell,Eija Honkavaara

doi:10.3390/s120100453

Abstract

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

Highlights

In recent years unmanned aerial vehicles (UAVs) have entered the field of aerial imaging.Low operation and hardware costs with low altitude UAVs compared to the aerial imaging with high quality mapping sensors from manned airborne platforms have made UAVs an attracting choice for aerial photogrammetry in many application areas [1,2]
The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation
In the case of UltraCamXp, the 60% forward overlap was not ideal for matching in forest areas. These results show that the results of the UAV-carried small-format camera were comparable to a large-format photogrammetric camera in relative point densities for automatically measured point clouds

Summary

Introduction

Low operation and hardware costs with low altitude UAVs compared to the aerial imaging with high quality mapping sensors from manned airborne platforms have made UAVs an attracting choice for aerial photogrammetry in many application areas [1,2]. There already exist several new approaches for high-quality, dense point cloud generation from passive image data by image matching [13,14,15,16]. Image based point clouds are an attracting alternative for laser scanning, because they enable high precision 3D mapping applications for low-cost and low-weight systems. Laser scanners, which require high precision Global Navigation Satellite System and inertial measurement unit (GNSS/IMU), cannot be operated currently from very low-weight UAV platforms [17,18]

Objectives

Results

Conclusion