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The proposed pose estimation method does not exceed an average distance error of 3 cm while being capable of providing pose estimates at up to 60 FPS on recent hardware.
Experimental results show greatly improved pose estimates with the proposed sensor fusion.
Finally, we use a fast loop closure approach to reduce drifts and obtain global pose estimates.
Current approaches rely heavily on vehicle pose estimates to prompt loop closure.
Hence, all pose estimates up to the present time will benefit from this update.
A Kalman filter can only contribute to a limited extend to the total accuracy of the pose estimates.
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(a): No filter, first order hold on pose estimate from the camera pose algorithm.
By calibrating the ground plane at each frame, we show that a partial pose estimate can be recovered.
A good map is necessary for localization while an accurate pose estimate is needed for map reconstruction.
Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the best robot's pose estimate.
This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate.
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