Complementary filtering approach to orientation estimation using inertial sensors only

Precise and reliable estimation of orientation plays crucial role for any mobile robot operating in unknown environment. The most common solution to determination of the three orientation angles: pitch, roll, and yaw, relies on the Attitude and Heading Reference System (AHRS) that exploits inertial data fusion (accelerations and angular rates) with magnetic measurements. However, in real world applications strong vibration and disturbances in magnetic field usually cause this approach to provide poor results. Therefore, we have devised a new approach to orientation estimation using inertial sensors only. It is based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing. In the final, the algorithm well outperformed the commercial AHRS solution based on magnetometer aiding.

[1]  Xiaoming Hu,et al.  Drift-free attitude estimation for accelerated rigid bodies , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[2]  Marko Topič,et al.  Calibration and data fusion solution for the miniature attitude and heading reference system , 2007 .

[3]  Qiang Qu,et al.  Research of Attitude Estimation of UAV Based on Information Fusion of Complementary Filter , 2009, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology.

[4]  D. Gebre-Egziabher,et al.  A low-cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications , 1998, IEEE 1998 Position Location and Navigation Symposium (Cat. No.98CH36153).

[5]  Eun-Hwan Shin,et al.  Accuracy Improvement of Low Cost INS/GPS for Land Applications , 2002 .

[6]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[7]  D. W. Allan,et al.  Statistics of atomic frequency standards , 1966 .

[8]  Yanhua Zhang,et al.  Adaptive filter for a miniature MEMS based attitude and heading reference system , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[9]  M. Moallem,et al.  Attitude estimation and stabilization of a rigid body using low-cost sensors , 2007, 2007 46th IEEE Conference on Decision and Control.

[10]  Matej Hoffmann,et al.  Dead reckoning in a dynamic quadruped robot: Inertial navigation system aided by a legged odometer , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  Michal Reinstein,et al.  Error Analyses of Attitude and Heading Reference Systems , 2009 .

[12]  P. Savage.  Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms , 1998 .

[13]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[14]  Michal Reinstein,et al.  Algorithms for heading determination using inertial sensors , 2010 .

[15]  P. Savage.  STRAPDOWN INERTIAL NAVIGATION INTEGRATION ALGORITHM DESIGN. PART 2: VELOCITY AND POSITION ALGORITHMS , 1998 .

[16]  Abdelaziz Benallegue,et al.  Attitude estimation with gyros-bias compensation using low-cost sensors , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[17]  Pavel Paces,et al.  Flight attitude track reconstruction using two AHRS units under laboratory conditions , 2009, 2009 IEEE Sensors.

[18]  M. Bodnicki,et al.  Sensing Tilt With MEMS Accelerometers , 2006, IEEE Sensors Journal.

[19]  Xiaoping Yun,et al.  Adaptive-gain complementary filter of inertial and magnetic data for orientation estimation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[20]  Milos Sotak.  Coarse alignment algorithm for ADIS16405 , 2010 .

[21]  Isaac Skog,et al.  Calibration of a MEMS inertial measurement unit , 2006 .