An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization

Ehsan Khoramshahi,Niko Koivumäki,Eija Honkavaara,Raquel A Oliveira

doi:10.3390/rs12193185

Abstract

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.

Highlights

Unmanned aerial vehicles (UAVs) are energy-efficient observers with a high degree of freedom
Modern UAVs are equipped with a wide range of sensors and receivers such as optical sensors, e.g., red green blue (RGB) cameras, miniaturized hyper-spectral sensors, global positioning receivers, e.g., a global navigation satellite system (GNSS), orientation sensors such as an inertial measurement unit (IMU), and light detection and ranging (Lidar) sensors [3]
scale invariant feature transform (SIFT) demonstrated the best distribution of match points among all the other methods, it required a considerable amount of random-access memory (RAM), especially in comparison to oriented FAST and rotated BRIEF (ORB) and multiwindow SIFT

Summary

Introduction

Unmanned aerial vehicles (UAVs) are energy-efficient observers with a high degree of freedom. Real-time trajectory estimation of a UAV is essentially a micro task for many advanced applications, e.g., smart city [4], emergency [5] or agricultural and forestry [6]. Many commercial UAV models nowadays have acceptable flight durability for most urban and forestry applications. They are usually equipped with autopilot systems that ease the operation of a drone in complex situations [19]. Coplanarity equations state that an ideal object point, its corresponding image points, and focal centers of cameras lie in a hypothetical plane under the assumption that image distortions and noises are removed. Collinearity equations state that an image point, a focal center, and a corresponding object point lie on a line [46]. Coplanarity deals with a stereo pair in comparison to the collinearity that allows for multi-image adjustment

Objectives

Methods

Results

Conclusion