Robot localisation in neighbourhood environments situated in equatorial regions presents many different challenges to those found elsewhere, buildings are sparse and the surrounding areas are covered by dense vegetation. In this paper, a Bayesian formulated framework that uses a road network topology in the form of a map to constrain the simultaneous localization and mapping (SLAM) solution is presented. The implementation uses a Rao-Blackwellized particle filter with adaptive particle sampling sets to optimise computation. The rationale is to bind effectively the adaptive sample-based representation of robot localisation estimation using a road network map whilst detecting and mapping distinct features found at the roadsides. This allows the reduction of the number of samples for ease of computation. In addition the particle depletion problem that compromises the robot closing large loops is minimized. The effectiveness of the approach is demonstrated via experimentation on a vehicle test-bed travelling in a university campus road network.