Incremental distributed inference from arbitrary poses and unknown data association: Using collaborating robots to establish a common reference

Abstract

High-accuracy localization is a fundamental capability that is essential for autonomous reliable operation in numerous applications, including autonomous driving, monitoring of an environmental phenomena, mapping, and tracking. The problem can be formulated as inference over the robot's state and possibly additional variables of interest based on incoming sensor measurements and a priori information, if such information exists. Moreover, in numerous applications, this inference problem has to be solved in real time, thus requiring computationally efficient inference methods.