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The scan-matching-based motion model shows more accurate results than when using odometry alone.
The target motion model shows the relationship between y 1 and y n : y 1 = y n − n − 1 T V y x 1 = x n − n − 1 T V x (9).
Only at the very end of our simulation, where the person walked to the exit of the building and went outside, the motion model shows improvements: due to the weighting function the particles get more directed to the straight path outside.
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Section 4.1 formulates the black-border constraints as linear inequalities for most of the 2D camera motion models, and shows how estimate projection can be used to solve the constrained estimation problem.
The outputs of the scenario where no motion model is used are shown in Figure 9.
On the other hand, the results of the scenario where our maps-based motion model is used are shown in Figure 10.
As expected and discussed in Section 2.1, the two clouds scenario where the maps-based motion model is used has shown much lower average position error compared to the case where only walls are used.
The average position error in Figure 8 where our motion model is used is again shown here (blue curve) for comparison.
No.2 (VWC) model shows the smallest motion amplitudes in waves, but needs the greatest power at high speed.
The red curve shows the average position error of the estimator when our developed motion model is used, while the black curve shows the error when binary walls restrictions are used as a replacement for the motion model.
We will show that the motion model will help for obtaining better performance in critical navigation scenarios where two or more modes may be competing for some of the time (multi-modal scenarios).
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