Place recognition in dynamic environments

We have developed a technique for place learning and place recognition in dynamic environments. Our technique associates evidence grids with places in the world and uses hill climbing to find the best alignment between current perceptions and learned evidence grids. We present results from five experiments performed using a real mobile robot in a real-world environment. These experiments measured the effects of transient and lasting changes in the environment on the robot's ability to localize. In addition, these experiments tested the robot's ability to recognize places from different viewpoints and verified the scalability of this approach to environments containing large numbers of places. Our results demonstrate that places can be recognized successfully despite significant changes in their appearance, despite the presence of moving obstacles, and despite observing these places from different viewpoints during place learning and place recognition.