Name
Abstract | ||
|---|---|---|
This paper proposes a new method for occupancy map building using a mixture of Gaussian processes. We consider occupancy maps as a binary classification problem of positions being occupied or not, and apply Gaussian processes. Particularly, since the computational complexity of Gaussian processes grows as O(n3), where n is the number of data points, we divide the training data into small subsets and apply a mixture of Gaussian processes. The procedure of our map building method consists of three steps. First, we cluster acquired data by grouping laser hit points on the same line into the same cluster. Then, we build local occupancy maps by using Gaussian processes with clustered data. Finally, local occupancy maps are merged into one by using a mixture of Gaussian processes. Simulation results will be compared with previous researches and provided demonstrating the benefits of the approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICRA.2012.6225355 | ICRA |
Keywords | Field | DocType |
occupancy map building,binary classification problem,pattern classification,mobile robots,set theory,map building method,gaussian process mixture,computational complexity,path planning,gaussian processes,training data,binary classification,data models,data model,mixture of gaussians,uncertainty,gaussian process,process simulation,kernel | Kernel (linear algebra),Data point,Data mining,Data modeling,Binary classification,Control theory,Algorithm,Occupancy,Gaussian process,Mixture model,Mathematics,Computational complexity theory | Conference |
Volume | Issue | ISSN |
2012 | 1 | 1050-4729 E-ISBN : 978-1-4673-1404-6 |
ISBN | Citations | PageRank |
978-1-4673-1404-6 | 6 | 0.45 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| SooHwan Kim | 1 | 60 | 8.05 |
| Jong-hyuk Kim | 2 | 198 | 28.85 |