Continuous occupancy maps using overlapping local Gaussian processes

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This paper presents an efficient method of building continuous occupancy maps using Gaussian processes for large-scale environments. Although Gaussian processes have been successfully applied to map building, the applications are limited to small-scale environments due to the high computational complexity. To improve the scalability, we adopt a divide and conquer strategy where data are partitioned into manageable size of clusters and local Gaussian processes are applied to each cluster. Particularly, we propose overlapping clusters to mitigate the discontinuity problem that predictions of local estimators do not match along the boundaries. The results are consistent and continuous occupancy voxel maps in a fully Bayesian framework. We evaluate our method with simulated data and compare map accuracy and computational time with previous work. We also demonstrate our method with real data acquired from a laser range finder.