Storing and recalling information for vision localization

In implementing a vision localization system, a crucial issue to consider is how to efficiently store and recall the necessary information so that the robot is not only able to accurately localize itself, but does so in a timely manner. In the presented system, we discuss a strategy to minimize the amount of stored data by analyzing the strengths and weaknesses of several cooperating recognition modules, and by using them through a prioritization scheme, which orders the data entries from the most likely to match to the least. We validate the system is a series of experiments at three large scale outdoor environments: a building complex (126 times 180 ft. area, 3583 testing images), a vegetation-filled park (270 times 360 ft. area, 6006 testing images), and an open-field area (450 times 585 ft. area, 8823 testing images) - each with its own set of challenges. Not only is the system able to localize in these environments (on average 3.46 ft., 6.55 ft. 12.96 ft. of error, respectively), it does so while searching through only 7.35%, 3.50%, and 6.12% of all the stored information, respectively.