Localization Life in the Atacama 2004 Science & Technology Workshop January 6-7, 2005 Daniel Villa Carnegie Mellon Matthew Deans QSS/NASA Ames

Life in the Atacama 2004 Workshop1NASA Ames Research Center Carnegie Mellon Basic Description Sensing INS: 3 axis accel, 3 axis gyro Sun sensor Encoders Inclinometer FOG Motion commands Estimation Kalman Filter Nonlinear smoothing Dedicated PC-104 stack. Goals: Accuracy 5% distance traveled Orientation within 3° Odometry within 2% of distance traveled

Life in the Atacama 2004 Workshop2NASA Ames Research Center Carnegie Mellon Block Diagram

Life in the Atacama 2004 Workshop3NASA Ames Research Center Carnegie Mellon Sun Sensor Now includes integrated inclinometer

Life in the Atacama 2004 Workshop4NASA Ames Research Center Carnegie Mellon Sun Sensor

Life in the Atacama 2004 Workshop5NASA Ames Research Center Carnegie Mellon Sun Sensor Camera model error: 0.5 pixel RMS RMS for 3 dimensions of rotation Integration: Problems with h/w and s/w integration In the field: A few degrees Obvious systematic errors: calibration?

Life in the Atacama 2004 Workshop6NASA Ames Research Center Carnegie Mellon Dead Reckon Estimator Straightforward path integration Relied only on data from sensors Encoders FOG Roll-Pitch Does not use: IMU Sun tracker

Life in the Atacama 2004 Workshop7NASA Ames Research Center Carnegie Mellon Dead Reckon Results

Life in the Atacama 2004 Workshop8NASA Ames Research Center Carnegie Mellon Kalman Filter rover model system inputs (speed, radius) system outputs (sensors) predicted outputs (sensors) _ predicted state (x, y, z, roll, pitch, yaw) K updated state +

Life in the Atacama 2004 Workshop9NASA Ames Research Center Carnegie Mellon Kalman Results NaN

Life in the Atacama 2004 Workshop10NASA Ames Research Center Carnegie Mellon Nonlinear Smoothing Performed when robot is stationary Operates on a sub-sampled sensor dataset Revises movement history New pose and covariance fed back into filter

Life in the Atacama 2004 Workshop11NASA Ames Research Center Carnegie Mellon Nonlinear Smoothing Results FilteringSmoothing Heading correction propagates to corrected position Simulation

Life in the Atacama 2004 Workshop12NASA Ames Research Center Carnegie Mellon Next Steps: Sun Sensor: Early specification of interfaces Better coordination of efforts Estimator work: Kalman Filter debugging, improvements Comparison of Kalman vs dead reckon Real-time Linux kernel

Life in the Atacama 2004 Workshop13NASA Ames Research Center Carnegie Mellon Next Steps: Visual Odometry Accuracy of ~0.1% 1mm over 1m 1cm at 5-10m Critical element of single cycle instrument placement Could enable some return to site/point capability

Life in the Atacama 2004 Workshop14NASA Ames Research Center Carnegie Mellon Next Steps: Visual Odometry

Life in the Atacama 2004 Workshop15NASA Ames Research Center Carnegie Mellon end