Error analysis of algorithms for camera rotation calculation in GPS/IMU/camera fusion for UAV sense and avoid systems

In this paper four camera pose estimation algorithms are investigated in simulations. The aim of the investigation is to show the strengths and weaknesses of these algorithms in the aircraft attitude estimation task. The work is part of a research project where a low cost UAV is developed which can be integrated into the national airspace. Two main issues are addressed with these measurements, one is the sense-and-avoid capability of the aircraft and the other is sensor redundancy. Both parts can benefit from a good attitude estimate. Thus, it is important to use the appropriate algorithm for the camera rotation estimation. Results show that many times even the simplest algorithm can perform at an acceptable level of precision for the sensor fusion.

[1]  József Bokor,et al.  Towards real-time visual and IMU data fusion , 2014 .

[2]  Dennis M. Akos,et al.  Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments , 2012, Sensors.

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  H. G. Wolf,et al.  Unmanned aircraft systems integration into the national airspace , 2013, 2013 IEEE Aerospace Conference.

[5]  Bálint Vanek,et al.  Performance analysis of a vision only sense and avoid system for small UAVs , 2011 .

[6]  S. J. Farmer,et al.  Architecting UAV sense & avoid systems , 2008 .

[7]  Bálint Vanek,et al.  Safety critical platform for mini UAS insertion into the common airspace , 2014 .

[8]  Domenico Prattichizzo,et al.  EGT for multiple view geometry and visual servoing: robotics vision with pinhole and panoramic cameras , 2005, IEEE Robotics & Automation Magazine.

[9]  Demoz Gebre-Egziabher,et al.  Performance comparison of tight and loose INS-camera integration , 2011 .

[10]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  Sunglok Choi,et al.  Performance Evaluation of RANSAC Family , 2009, BMVC.

[12]  Timothy W. McLain,et al.  Small Unmanned Aircraft: Theory and Practice , 2012 .

[13]  David Nister,et al.  Recent developments on direct relative orientation , 2006 .

[14]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[15]  William K. Pratt,et al.  Digital Image Processing: PIKS Inside , 2001 .

[16]  Bálint Vanek,et al.  A Five-Camera Vision System for UAV Visual Attitude Calculation and Collision Warning , 2013, ICVS.

[17]  Bálint Vanek,et al.  Visual Detection and Implementation Aspects of a UAV See and Avoid System , 2011, 2011 20th European Conference on Circuit Theory and Design (ECCTD).

[18]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[19]  Bálint Vanek,et al.  Collision avoidance for UAV using visual detection , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[20]  Giancarmine Fasano,et al.  Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems , 2012 .

[21]  Yew Chai Paw.  Synthesis and validation of flight control for UAV. , 2009 .