LAUSR

Maps provide an important source of information for autonomous vehicles. They can be used along with cameras and lidars to localize the vehicle. This requires the ability to correctly associate observations to features referenced in the map. The problem is all the more difficult that all observations are not necessarily referenced, and all map features might not be detectable with the embedded sensors. This letter presents an adjustment technique than enables to increase the number of associations that can be made while limiting the chance of obtaining wrong associations. This is achieved by matching observations in batches in a buffer and matching them regularly. Periodically, the observation buffer is used to adjust the trajectory used to match observations. This is done without making any assumption on the association between observations and map features through a likelihood maximization process. The adjusted trajectory then provides the best associations that are used for real-time localization. The method was tested with data recorded on public roads using an experimental vehicle. The results show that the number of associations that can be made is increased, thanks to the trajectory adjustment step and the use of an observation buffer. This also results in greater localization accuracy and consistency with an average error of $0.7\text{ meters}$ at 50 Hz using road markings and traffic signs.