Integrating asynchronous observations for mobile robot position tracking in cooperative environments

This paper presents an asynchronous particle filter algorithm for mobile robot position tracking, taking into account time considerations when integrating observations being delayed or advanced from the prior estimate time point. The interest of that filter lies in cooperative environments and in fast vehicles. The paper studies the first case, where a sensor network shares perception data with running robots that receive accurate observations with large delays due to acquisition, processing and wireless communications. Promising simulated results comparing a basic particle filter and the proposed one are shown. The paper also investigates a situation where a robot is tracking its position, fusing only odometry and observations from a camera network partially covering the robot path.