Recovering dense 3D information from intra-operative endoscopic images together with the relative endoscope camera pose are fundamental blocks for accurate guidance and navigation in image-guided surgery. They have several important applications, e.g., augmented reality overlay of pre-operative models. This thesis provides a systematic approach for estimating these two pieces of information based on a pure vision Simultaneous Localization And Mapping (SLAM). We decouple the dense reconstruction from the camera trajectory estimation, resulting in a system that combines the accuracy and robustness of feature-based SLAM with the more complete reconstruction of direct SLAM methods. The proposed solutions in this thesis have been validated on real porcine sequences from different datasets and proved to be fast and do not need any external tracking hardware nor significant intervention from medical staff. The sole input is video frames of a standard monocular endoscope.