  The development of data innovation as of late and the expanded limit, has
permitted the acquaintance of artificial vision connected with SLAM, offering
ascend to what is known as Visual SLAM. The objective of this paper is to build
up a route framework dependent on Visual SLAM to get a robot to a fundamental
and new condition, have the capacity to set and make a three-dimensional guide
thereof, utilizing just as sources of info recording your way with a stereo
vision camera. The consequence of this analysis is that the framework Visual
SLAM together with the combination of Fast SLAM (combination of kalman with
particulate filter and SIFT) perceive and recognize characteristic points in
images so adequately exact and unambiguous. This framework uses MATLAB, since
its adaptability and comfort for performing a wide range of tests. The program
has been tested by inserting a prerecorded video input with a camera stereo in
which a course is done by an office environment. The algorithm initially
locates points of interest in a stereo frame captured by the camera. These will
be located in 3D and they associate an identification descriptor. In the next
frame, the camera likewise identified points of interest and it will be
compared which of them have been previously detected by comparing their
descriptors. This process is known as "data association" and its successful
completion is fundamental to the SLAM algorithm. The position data of the robot
and points interest stored in data structures known as "particles" that evolve
independently. Its management is very important for the proper functioning of
the algorithm Fast SLAM. The results are found to be satisfactory.
