G. Casalino, E. Zereik, E. Simetti, A. Turetta, S. Torelli and A. Sperindè EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Introduction Visual Odometry Additional Measurements State Estimators Sequence Estimators Multi-Sensor Integration Discussion and ConclusionAgenda EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Planetary Robot accurate localization and motion estimation Different techniques WO IMU VO Improve VO additional image processing Extended/Iterated Kalman Filters Sequence Estimators Integration scheme Final multi-sensor schemeIntroduction EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Visual Odometry EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Visual Odometry EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Visual Odometry positionorientation At each step, the rover position and orientation are computed with respect to the previous step Sequence of positions-orientations truly attained by the vehicle EUCASS 2011 – 4-8 July, St. Petersburg, Russia

measured rover position and orientation independent white noise sequences affecting position and orientation measurements position and orientation covariance matricesEstimations

EUCASS 2011 – 4-8 July, St. Petersburg, Russia relative At each step, VO provides the rover relative position and orientation Absolute Absolute rover position and orientation: whereEstimations

open chain independent Linear open chain of frames, with independent positioning Error increasing Error progressively increasing with the number of stages No further constraints Improvements: Additional measurements At each step provide measurements of the occurred motion between frames andEstimations EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Assumption: stereo camera can recognize in the current frame a sufficient number of features belonging to images and Additional Measurements EUCASS 2011 – 4-8 July, St. Petersburg, Russia

orientation position State Space Model EUCASS 2011 – 4-8 July, St. Petersburg, Russia

State Space Model EUCASS 2011 – 4-8 July, St. Petersburg, Russia

System evolution estimated via standard Kalman filters Extended Extended (EKF) Iterated Iterated (IKF) Gaussianity Gaussianity hypothesis for the filter Gaussianity is not suitable due to system non-linearity suboptimality such an approximation leads to suboptimality recursive These are recursive filters linearly increasing errors have still to be expected for increasing number of stages Incoming acquisitions to better all the past state estimations State Estimators EUCASS 2011 – 4-8 July, St. Petersburg, Russia

The problem becomes: measurement sequence sequence state sequence Sequence Estimators EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Renew, at each stage, the entire sequence, without relying on the previous one strongly reduced Error generally characterizing IKF and EKF should result strongly reducedBUT dimensionality Increasing dimensionality with increasing number of stages linear linear and quite acceptable with a reasonably high maximum number of stages freeze restart after this freeze and restart the procedure Sequence Estimators EUCASS 2011 – 4-8 July, St. Petersburg, Russia

sub-problems Solve the following cascade of sub-problems: manageable Maintain a manageable implementative form that otherwise cannot be guaranteed, considering the general problem Minimization of the Gaussian p.d.f. exponents Bayes Bayes formula Sequence Estimators EUCASS 2011 – 4-8 July, St. Petersburg, Russia

linear parametrization At each stage a linear parametrization is obtained It is the constraint for the previous stage Back Substitution Back Substitution scheme Dynamic Programming Dynamic Programming strategy Sequence Estimators EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Wait until stage Reneweach Renew the interpolated sequence in correspondence of each new stage Backward Phase Backward Phase: computational effort increasing with the number of stages Restart Restart the procedure from the last stage considered as the new initial one Smaller Smaller drifting errors suboptimality Accepted suboptimality vs. joint estimation of both sequencesComments EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Exploit different sensors Augmented sensor Augmented sensor with better performances Different integration schemes: Integration Scheme EUCASS 2011 – 4-8 July, St. Petersburg, Russia

sequential First sequential scheme smaller variance SQE fed with smaller variance measurements data from STE Feedback loop used to re-initialize the STE module Totally useless Totally useless scheme Integration Scheme

parallel Without further data, things can be bettered with a parallel integration scheme EUCASS 2011 – 4-8 July, St. Petersburg, Russia Integration Scheme

IMU IMU: measurements about the angular velocity vector and the linear acceleration vector WO WO integration EUCASS 2011 – 4-8 July, St. Petersburg, Russia Multi-Sensor Integration

Previously developed VO module via software CUDA implementation: via CUDA SURF extraction and descriptors via CUDA matching and tracking via software with SAD pose estimation via software Sequence Estimator Sensor data integration Discussion and Conclusion EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Open issues: under consideration still under consideration many simulations and experimental tests are to be carried out not only planar motion mange the non-holonomic constraints for the rover “Visual Odometry Centric” scheme different state space model starting from a different sensor integration scheme with possibly different characteristics worth a comparison Discussion and Conclusion EUCASS 2011 – 4-8 July, St. Petersburg, Russia

Questions? Thank you! EUCASS 2011 – 4-8 July, St. Petersburg, Russia