Non-stationary dependent Gaussian processes for data fusion in large-scale terrain modeling
Obtaining a comprehensive model of large and complex terrain typically entails the use of both multiple sensory modalities and multiple data sets. This paper demonstrates the use of dependent Gaussian processes for data fusion in the context of large scale terrain modeling. Specifically, this paper derives and demonstrates the use of a non-stationary kernel (Neural Network) in this context. Experiments performed on multiple large scale (spanning about 5 sq km) 3D terrain data sets obtained from multiple sensory modalities (GPS surveys and laser scans) demonstrate the approach to data fusion and provide a preliminary demonstration of the superior modeling capability of Gaussian processes based on this kernel.
Shrihari Vasudevan Fabio Ramos Eric Nettleton Hugh Durrant-Whyte
Australian Centre for Field Robotics,University of Sydney,NSW 2006,Australia
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
1875-1882
2011-05-09(万方平台首次上网日期,不代表论文的发表时间)