Monocular RGB camera and distance-limited RGB-D camera with Microsoft Kinect system are respectively utilized to solve the 6DoF navigation problem for indoor robots. At first, based on the traditional partial-DoF pose estimation algorithms, an incremental parameterized model is proposed for feature-points' parameters, which is able to solve the scale ambiguity problem. This model differs from Euclid and inverse depth parameterized models with the sharply reduced state-dimension and the consistent observability of system state. In addition, based on this proposed incremental parameterized model, the 6DoF motion is estimated by the observed image-sequence that comes from the RGB camera or infrared camera. At the end of this paper, both the RGB and depth image sequences that sampled from the Kinect system are utilized in Euclid parameterized model and the incremental parameterized model. And the corresponding results validate the effectiveness of the proposed autonomous navigation approach.