The invention relates to a
simultaneous localization and mapping method based on distributed edge
unscented particle filter. First, a coordinate
system is built and an environmental map is initialized; then subfilters are built for each
landmark point with successful matching respectively; next, based on a
robot motion model, a particle swarm is generated in each subfilter respectively, and the
state vector and the variance of each particle are obtained;
noise is introduced, particle state vectors after extension are calculated by utilization of unscented transformation, the particles after extension are updated and the particle swarms are optimized; then particle weights are calculated and normalization is carried out, and aggregated data of each subfilter are subjected to statistics and the data are sent to a master filter; next, global
estimation and variance are calculated; then the effective sampling draw scale and sampling threshold of each subfiter are determined, the subfilters with severe particle degeneracy are subjected to
resampling; then the state vectors and the variances of the
robot are output, and stored in a map. Finally,
landmark point states are updated by utilization of
kalman filtering algorithm until the
robot is no longer running.