چکیده (انگلیسی):

In this work, we study the pose estimation problem of
anautonomous mobile robot. Particularly, we compare the Extended
Kalman Filter (EKF) to Lowe’s method based on the iterative Newton’s
method for solving a system of nonlinear equations. Although the EKF is
recursive which renders it suitable for the real-time problem at hand,
Lowe’s method has much less dimensionality. This is the motivation for
comparing both approaches. We have used the stereo information for
obtaining the 3-D structure and outlier rejection. This has provided an
opportunity to weigh feeding both algorithms with single measurements
(from one camera) against feeding them with pair measurements (from
the stereo pair). We have studied the effects of using three ranges of
the number of features and the longevity on the accuracy of the obtained
pose parameters. Moreover, we have investigated the impact of the
number of iterations on the accuracy of Lowe’s method. An extensive set
of simulations as well as real experiments using various motion patterns
have been conducted. The main finding of this work is that Lowe’s
method (due to its low dimensionality) is much faster with
approximately the same accuracy. Besides, it can recover from a
situation which is close to singularity. On the other hand, the EKF
makes better use of multiple camera measurements which allows a
sustained performance even if one camera is off or occluded.