In implementing an INS/SLAM integrated navigation system based on the vision sensor, a suboptimal nonlinear filter is used to figure out the nonlinear characteristics in measurement and noise model. When a conventional centralized filter is used, however, the entire state vectors need to be reconfigured in every necessary cycle as the number of feature points changes, which is hard to isolate potential faults. Furthermore, any change in the number of feature points and a subsequent increase in the dimension of state variables may result in an exponential growth in computation quantities. In order to address these issues, this paper presents a distributed particle filter approach for implementing a vision sensor based INS/SLAM system. The proposed system has several local filters which are subject to change flexibly by the number of feature points, and separates state vectors into sub-states for vehicle dynamics and feature points so that minimum state vectors can be estimated in the master filter. Simulation results show that the distributed particle filter performs competitively as with the centralized particle filter and is capable of improving computation quantities.