Name
Title | ||
|---|---|---|
Occupancy mapping and surface reconstruction using local Gaussian processes with Kinect sensors. |
Abstract | ||
|---|---|---|
Although RGB-D sensors have been successfully applied to visual SLAM and surface reconstruction, most of the applications aim at visualization. In this paper, we propose a noble method of building continuous occupancy maps and reconstructing surfaces in a single framework for both navigation and visualization. Particularly, we apply a Bayesian nonparametric approach, Gaussian process classification, to occupancy mapping. However, it suffers from high-computational complexity of O(n(3))+O(n(2)m), where n and m are the numbers of training and test data, respectively, limiting its use for large-scale mapping with huge training data, which is common with high-resolution RGB-D sensors. Therefore, we partition both training and test data with a coarse-to-fine clustering method and apply Gaussian processes to each local clusters. In addition, we consider Gaussian processes as implicit functions, and thus extract iso-surfaces from the scalar fields, continuous occupancy maps, using marching cubes. By doing that, we are able to build two types of map representations within a single framework of Gaussian processes. Experimental results with 2-D simulated data show that the accuracy of our approximated method is comparable to previous work, while the computational time is dramatically reduced. We also demonstrate our method with 3-D real data to show its feasibility in large-scale environments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TCYB.2013.2272592 | IEEE T. Cybernetics |
Keywords | Field | DocType |
computational time,high-computational complexity,image representation,local gaussian processes,pattern clustering,gaussian processes,coarse-to-fine clustering method,marching cubes,mobile robot navigation,iso-surface extraction,mobile robots,kinect sensors,rgb-d mapping,image reconstruction,high-resolution rgb-d sensors,computational complexity,image sensors,feature extraction,bayes methods,data visualisation,path planning,map representations,test data,computer vision,visual slam,slam (robots),robotics communities,surface reconstruction,bayesian nonparametric approach,continuous occupancy mapping,continuous occupancy maps,robot vision,image colour analysis,training data | Iterative reconstruction,Data visualization,Visualization,Computer science,Marching cubes,Gaussian process,Artificial intelligence,Test data,Cluster analysis,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
43 | 5 | 2168-2275 |
Citations | PageRank | References |
13 | 0.63 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| SooHwan Kim | 1 | 60 | 8.05 |
| Jong-hyuk Kim | 2 | 198 | 28.85 |