Monocular visual SLAM based on Inverse depth parametrization

The first objective of this research has always been carry out a study of visual techniques SLAM (Simultaneous localization and mapping), specifically the type monovisual, less studied than the stereo. These techniques have been well studied in the world of robotics. These techniques are focused on reconstruct a map of the robot enviroment while maintaining its position information in that map. We chose to investigate a method to encode the points by the inverse of its depth, from the first time that the feature was observed. This method permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).At first, the study mentioned it should be consolidated developing an application that implements this method. After suffering various difficulties, it was decided to make use of a platform developed by the same author of Slam method mentioned in MATLAB. Until then it had developed the tasks of calibration, feature extraction and matching. From that point, that application was adapted to the characteristics of our camera and our video to work. We recorded a video with our camera following a known trajectory to check the calculated path shown in the application. Corroborating works and studying the limitations and advantages of this method