Autonomous mobile robots are a common trend in robotics these days. They characterize for combining their mobile abilities and perception capacities in an intelligent way. This paper explains the work done to achieve two basic issues in autonomous navigation: position tracking (localization) and path planning. It starts with an odometric model of robot as an approximation to a differential drive mobile, including probabilistic modeling for uncertainty evolution. Next, based on the Extended Kalman Filter, it continues to show a form to integrate odometric predictions with sensor data, so that pose belief could pass through measurement update. Once the mobile position is tracked, robot is able to follow a trajectory towards a specific position objective, avoiding roadblocks in the way. This trajectory would be computed using a “Base Points” method for path planning. Finally, results of the implementation of the navigation system are presented.