Simultaneous localization and mapping (SLAM) for mobile robots has attracted research interests in the past two decades. Recent years, Rao-Blackwellized particle filter (RBPF) approach proved to be an effective means to estimate the full SLAM posterior. However, most of the SLAM practices are implemented in static environment. To navigate in dynamic environment, model based approaches have been implemented. The approaches require modeling, classification, and data association. Modeling and classification often require prior knowledge of the environment and are less robust in highly complex environment. To obviate modeling, classification and data association in dynamic environment, a solution framework based on Rao-Blackwellised genetic algorithmic filter (RBGAF) is extended for recovering the full SLAM posterior, using raw sensor measurements. The resultant extended RBGAF-SLAM recovers the negative sensor information during SLAM so that the moving objects can be efficiently identified in the particle maps. This approach permits the implementation of SLAM without prior knowledge of the environment and the static environment assumption. Simulations and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.
['Jun Feng Dong', 'W. Sardha Wijesoma', 'Andrew P. Shacklock']
Extended rao-blackwellised genetic algorithmic filter SLAM in dynamic environment with raw sensor measurement