With this article, we have addressed some issues in pose estimation with geometrical features and a modelbased approach in the context of monocular vision. While the 3-D tracking/estimation may be performed with optimal estimators (with the Kalman filter and its extended/nonlinear versions or with the particle filter also referred to as the sequential Monte Carlo method), system models and state vectors need the pose parameters recovery or the 3-D motion recovery from the motion field. The pose determination is needed for applications with high accurate 3-D positioning requirements, when occlusions, shadows or abrupt motions have to be handle. To this purpose, several geometrical featurebased approaches have been reviewed for solving the pose with various constrained degrees of freedom. Cue integration leads to robustness and automatic measurement of scene complexity. This is of prime importance to exploit the video information captured in a complex environment with dynamical changes. Composite features, colour, edges, texture integration are some additional data which have brought significant improvements of the tracker's behaviour thanks to robust and Mestimators. This is a key factor of success for a visionbased module with some autonomous capabilities, hence for the achievement of some visionbased (semi) autonomous tasks.