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The site execution times for each subset size for all three datasets are presented in boxplot form in Fig. 2. They verify the hypothesis presented in "Internal scheduler" subsection, we observe that the fastest execution time is achieved when the subset size n is set close to our empirical estimation of (0.01,%) of the total dataset size.
As expected result, where the execution time mostly depends on the number of expanded states and visited states, all of our proposed methods are faster than the original one in existing work and our new proposed bitwise-based indexing approach achieves the fastest execution time (Fig. 6c).
We also find that FindPath has the fastest execution time, the algorithm required less than 5 min to compute the full network.
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Optimized DSP-based algorithm has the fastest execution times.
The optimal value of n leading to the fastest execution times is (0.01,%) of the input dataset.
The results show that gzip provided the best compression ratios while 2D had the fastest execution times.
It may result in a faster execution time compared to the synchronous case.
The proposed method presented greater accuracy and faster execution time than the traditional method by visual inspection.
Full FOV and planar representation of the map gives the proposed SLAM system the capability to map large-scale environments while maintaining fast execution time.
In order to accelerate the algorithm, one can proceed with the parallelization of the algorithm and/or map it directly onto hardware to achieve faster execution time.
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