Large-scale continuous 2.5D robotic mapping
Liye Sun · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · 2018
Autonomous robotic systems require building representations of the environment in order to accomplish their particular tasks.Creating rich, continuous probabilistic maps is essential for the robot to perceive the world.As the complexity of the task increases, robots need more sensors or more data to build maps.Noisy and incomplete data is common for the robotic sensors outputs; Gaussian Process (GP), a flexible and powerful statistical model, has become a popular method to cope with the incompleteness of sensory information, incorporate and handle uncertainties appropriately and allow a multi-resolution representation of space.GP regression has been applied in robotic mapping to predict spatial correlations and fill in gaps in unknown areas across the field.The key component of GP for robotic mapping is that it captures spatial correlations and thus increases the accuracy of the representation when fusing data.When multiple sources of data are available, spatial correlations also can be used in fusion to improve accuracy.For large datasets, however, exploiting correlations can become prohibitively expensive.One attractive strategy for reducing storage and computational cost is submapping, which works by dividing the environment into small regions.If no information is shared between maps, submaps are statistically independent.This thesis investigates how to effectively and efficiently model the necessary spatial correlations that are required to build accurate large-scale maps.Three near-optimal probabilistic mapping frameworks that exploit global and local strategies such as submapping are proposed.SubGPBF vi