SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Sparse on-line Gaussian processes
Neural Computation
Constructing 3D City Models by Merging Aerial and Ground Views
IEEE Computer Graphics and Applications
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
The Journal of Machine Learning Research
Gaussian process dynamic programming
Neurocomputing
The New College Vision and Laser Data Set
International Journal of Robotics Research
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
Gaussian process modeling of large scale terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Space-carving Kernels for Accurate Rough Terrain Estimation
International Journal of Robotics Research
Adaptive data compression for robot perception
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
International Journal of Robotics Research
| Hi-index | 0.00 |
This paper concerns the creation of efficient surface representations from laser point clouds created by a push broom laser system. We produce a continuous, implicit, non-parametric and non-stationary representation with an update time that is constant. This allows us to form predictions of the underlying workspace surfaces at arbitrary locations and densities. The algorithm places no restriction on the complexity of the surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use of Gaussian Process Regression a natural choice. We adopt a formulation which handles the typical non-functional relation between XY location and elevation allowing us to map arbitrary environments. Results are presented that use real and synthetic data to analyse the trade-off between compression rate and reconstruction error. We attain decimation factors in excess of two orders of magnitude without significant degradation in fidelity.