![](bg7.png)
the others and the MCL with distance filter has higher
estimation error.
Figure 13: Trajectory error comparison for static obstacles
case.
Fig. 13 shows the case where static but unmod-
eled obstacles are presented in the environment. In
this case also the estimation error for the proposed al-
gorithm is clearly lower than the others and the MCL
with distance filter has higher estimation error.
6.5 Computation Complexity
The Table below shows the computation complexity
of the proposed technique compared with traditional
MCL and MCL with distance filter. It is very clear
that there is a very large reduction in computations.
The number represents how frequent the ray casting
function is called. It is normalized to standard MCL
value. The δ value was set to 0.7 in this table.
Table 1: Computation Complexity reduction table.
Standard MCL with MCL with
MCL Distance filter proposed scheme
1 1.994 0.12832
7 CONCLUSION AND FUTURE
WORK
In this research a modification to observation model
that is used in Monte Carlo localization has been pro-
posed. This modification reduces the peaks generated
in the observation likelihood function that is limiting
the performance of the localization process. Specif-
ically the peaks generated due to the invalidation of
the independent noise assumption between different
measurement beams.
The proposed scheme has been verified using Stage
simulator and ROS Robot operating system. the re-
sults shows improvement in both location estimation
error and the computations required for localization.
As future work, it is possible to find a reliable method
to calculate or estimate the conditional probabilities
in Eq.12 instead of neglecting some measurements.
Also it is important to find solutions to other causes
of the peaks in the observation model. This will defi-
nitely improves the localization farther.
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