Accurate and Robust Teach and Repeat Navigation by Visual Place Recognition: A CNN Approach

摘要

We propose a novel teach-and-repeat navigation system, SSM-Nav, which is based on the output of the recently introduced SSM visual place recognition methodology. During the teach phase, a teleoperated wheeled robot stores in a database features of images taken along an arbitrary route. During the repeat phase or navigation, a CNN-based comparison of each captured image is performed against the database. With the help of a particle filter, the image associated with the most likely location is selected at each time and its horizontal offset with respect to the current scene used to correct the steering of the robot and to navigate. Indoor tests in our lab show a maximum error of less than 10cm and excellent robustness to perturbations such as drastic changes in illumination, lateral displacements, different starting positions, or even kidnapping. Preliminary outdoor tests on a 0.22km route show promising results, with an estimated maximum error of less than 25cm.