  Environment perception is a crucial ability for robot's interaction into an
environment. One of the first steps in this direction is the combined problem
of simultaneous localization and mapping (SLAM). A new method, called G-SLAM,
is proposed, where the map is considered as a set of scattered points in the
continuous space followed by a probability that states the existence of an
obstacle in the subsequent point in space. A probabilistic approach with
particle filters for the robot's pose estimation and an adaptive recursive
algorithm for the map's probability distribution estimation is presented. Key
feature of the G-SLAM method is the adaptive repositioning of the scattered
points and their convergence around obstacles. In this paper the goal is to
estimate the best robot trajectory along with the probability distribution of
the obstacles in space. For experimental purposes a four wheel rear drive car
kinematic model is used and results derived from real case scenarios are
discussed.
