Mr. Guo Li Profile

Guo Li

at Nanjing Univ of Aeronautics and Astronautics

SPIE Involvement:

Author

Publications (1)

Proceedings Article | 18 August 2011 Paper

Ground moving target geo-location from monocular camera mounted on a micro air vehicle

Li Guo, Haisong Ang, Xiangming Zheng

Proceedings Volume 8194, 819419 (2011) https://doi.org/10.1117/12.900106

KEYWORDS: Micro unmanned aerial vehicles, Cameras, Optical flow, Sensors, Filtering (signal processing), Global Positioning System, Electronic filtering, Kinematics, Video, Databases

Read Abstract +

The usual approaches to unmanned air vehicle(UAV)-to-ground target geo-location impose some severe constraints to the system, such as stationary objects, accurate geo-reference terrain database, or ground plane assumption. Micro air vehicle(MAV) works with characteristics including low altitude flight, limited payload and onboard sensors' low accuracy. According to these characteristics, a method is developed to determine the location of ground moving target which imaged from the air using monocular camera equipped on MAV. This method eliminates the requirements for terrain database (elevation maps) and altimeters that can provide MAV's and target's altitude. Instead, the proposed method only requires MAV flight status provided by its inherent onboard navigation system which includes inertial measurement unit(IMU) and global position system(GPS). The key is to get accurate information on the altitude of the ground moving target. First, Optical flow method extracts background static feature points. Setting a local region around the target in the current image, The features which are on the same plane with the target in this region are extracted, and are retained as aided features. Then, inverse-velocity method calculates the location of these points by integrated with aircraft status. The altitude of object, which is calculated by using position information of these aided features, combining with aircraft status and image coordinates, geo-locate the target. Meanwhile, a framework with Bayesian estimator is employed to eliminate noise caused by camera, IMU and GPS. Firstly, an extended Kalman filter(EKF) provides a simultaneous localization and mapping solution for the estimation of aircraft states and aided features location which defines the moving target local environment. Secondly, an unscented transformation(UT) method determines the estimated mean and covariance of target location from aircraft states and aided features location, and then exports them for the moving target Kalman filter(KF). Experimental results show that our method can instantaneously geo-locate the moving target by operator's single click and can reach 15 meters accuracy for an MAV flying at 200 meters above the ground.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years