Kyoungah Choi

Developing a UAV-based Rapid Mapping System for Emergency Response

As disasters and accidents due to various natural or artificial causes being increased, the demands for rapid responses for emergency situations also have been ever-increasing. These emergency responses need to be not only more intelligent but also more customized to the individual sites to manage the emergency situations more efficiently. More effective counter-measures in such situations will be established only if more accurate and prompt spatial information about the changing areas due to the emergency are available. This information can be rapidly extracted from the airborne sensory data acquired by a UAV-based rapid mapping system. This paper introduces a Korean national project to develop a UAV-based rapid mapping system. The overall budget is about 6 million US dollars and the period is about four years. The goal of this project is to develop a light and flexible system at low cost to perform rapid mapping for emergency responses. This system consists of two main parts, aerial part and ground part. The aerial part includes a small UAV platform equipped with sensors (GPS/IMU/Camera/Laser Scanner) and sensor supporting modules for sensor integration, data transmission to the ground, data storages, time synchronization, and sensor stabilization. The ground part includes three sub-systems with appropriate software, which are a control/receiving/archiving subsystem, a data georeferencing subsystem, and a spatial information generation subsystem. As being in a middle stage of this project, we will present a brief introduction to the overall project and the design of the aerial system with its verification results.

A Sequential Aerial Triangulation Algorithm for Real-time Georeferencing of Image Sequences Acquired by an Airborne Multi-Sensor System

Real-time image georeferencing is essential to the prompt generation of spatial information such as orthoimages from the image sequence acquired by an airborne multi-sensor system. It is mostly based on direct georeferencing using a GPS/INS system, but its accuracy is limited by the quality of the GPS/INS data. More accurate results can be acquired using traditional aerial triangulation (AT) combined with GPS/INS data, which can be performed only as a post-processing method due to intense computational requirements. In this study, we propose a sequential AT algorithm that can produce accurate results comparable to those from the simultaneous AT algorithm in real time. Whenever a new image is added, the proposed algorithm rapidly performs AT with minimal computation at the current stage using the computational results from the previous stage. The experimental results show that the georeferencing of an image sequence at any stage took less than 0.1 s and its accuracy was determined within ± 5 cm on the estimated ground points, which is comparable to the results of simultaneous AT. This algorithm may be used for applications requiring real-time image georeferencing such as disaster monitoring and image-based navigation.

CCTV Coverage Index Based on Surveillance Resolution and Its Evaluation Using 3D Spatial Analysis

We propose a novel approach to evaluating how effectively a closed circuit television (CCTV) system can monitor a targeted area. With 3D models of the target area and the camera parameters of the CCTV system, the approach produces surveillance coverage index, which is newly defined in this study as a quantitative measure for surveillance performance. This index indicates the proportion of the space being monitored with a sufficient resolution to the entire space of the target area. It is determined by computing surveillance resolution at every position and orientation, which indicates how closely a specific object can be monitored with a CCTV system. We present full mathematical derivation for the resolution, which depends on the location and orientation of the object as well as the geometric model of a camera. With the proposed approach, we quantitatively evaluated the surveillance coverage of a CCTV system in an underground parking area. Our evaluation process provided various quantitative-analysis results, compelling us to examine the design of the CCTV system prior to its installation and understand the surveillance capability of an existing CCTV system.

High accurate affordable car navigation using built-in sensory data and images acquired from a front view camera

Nowadays cars are equipped with various built-in sensors such as speedometers, odometers, accelerometers, and gyros for safety and maintenance. Also, front view images can be economically acquired by a low-cost camera available in smartphones or black boxes. The combination of the built-in sensory data and the images can be an effective complement to a GPS based navigation. Therefore, we propose a car navigation framework to determine car position and attitude using the built-in sensory data such as a speed, angular rate and the images from a front view camera. The method consists of three steps, 1) dead reckoning using the velocity and yaw rate provided in real-time, 2) image georeferencing based on a sequential bundle adjustment using the dead reckoning results and 3) final estimation using a Kalman filter with the georeferencing results. The experimental results show that the proposed method can provide the positions with a reasonable accuracy level, which can be meaningful to complement a traditional GPS based navigation with a low cost.