Bathymetric particle filter SLAM using trajectory maps

Abstract

We present 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 with little to no overlap with previously explored terrain. To significantly reduce the memory requirements (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 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 in areas that may not have been directly observed previously. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared with dead reckoning fused with long-baseline (LBL) observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.