Development of a camera-aided optical mouse sensors based localization system for a free floating planar robot

Ground-based testbeds have been widely used for the development and verification on the ground of algorithms and space instrumentation. This paper presents the development of a contactless measurement system to estimate the planar motion of a free floating robot in the development phase of a ground-based testbed translational module. The contactless system is used to estimate both position and azimuth of the translational module relying on high-rate data, provided by an optical mouse sensors system, and low-rate measurements, provided by an external monocular vision system. An extended formulation of the Kalman filter is used to obtain a reliable estimation of the robot motion by fusing the two sets of measrements. Several tests are carried out to evaluate the accuracy of the system in different operative conditions and recommendations on how to improve the estimation accuracy are given.

[1]  Markus Schlotterer,et al.  Testbed for on-orbit servicing and formation flying dynamics emulation , 2010 .

[2]  Stefano Debei,et al.  SPARTANS - A cooperating spacecraft testbed for autonomous proximity operations experiments , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[3]  David W. Miller,et al.  Autonomous docking algorithm development and experimentation using the SPHERES testbed , 2004, SPIE Defense + Commercial Sensing.

[4]  Roland Siegwart,et al.  A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation , 2011, CVPR 2011.

[5]  Stefano Debei,et al.  Optical flow sensor based localization system for a cooperating spacecraft testbed , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

[6]  Marco Pertile,et al.  Uncertainty evaluation of a vision system for pose measurement of a spacecraft with fiducial markers , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[9]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Fumio Miyazaki,et al.  Self-localization for indoor mobile robots based on optical mouse sensor values and simple global camera information , 2005, 2005 IEEE International Conference on Robotics and Biomimetics - ROBIO.