Bathymetric SLAM with no map overlap using Gaussian Processes
Stephen Barkby, Stefan B. Williams, Oscar R. Pizarro, Michael V. Jakuba · 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011
This paper presents an efficient and featureless approach to Bathymetric Simultaneous Localization And Mapping (SLAM) that utilizes a Rao-Blackwellized Particle Filter (RBPF) and Gaussian Process (GP) Regression to provide loop closures in areas where little to no overlap with previously explored terrain is present. To significantly reduce the memory requirements of this approach (thereby allowing for the processing of large datasets) a novel map representation is also introduced that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronizes them to a common log of bathymetric observations. Upon detecting a loop closure each particle is then weighted by matching new observations to the current predictions generated from a local reconstruction of their map using GP Regression. Here the spatial correlation in the environment is fully exploited, allowing predictions of seabed depth to be generated in areas that may not have been directly observed previously. The particle resampling that is performed therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared to Dead Reckoning fused with Long-Baseline observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.