Translation And Scale Invariant Landmark Recognition Using Receptive Field Neural Networks

摘要

In this paper we present a neural net- work based approach to landmark recognition. Landmarks are used by mobile robots for navi- gation and self referencing information. Due to the motion of the robot, the location and size of the landmark in the sensor is always changing. The network architecture presented here is capable of overcoming changes in scale as well as position of the landmark. The neu- ral network is directly fed with intensity im- ages. This is a significant reduction in compu- tation overhead since no high-level image pro- cessing is needed. Since robot motion in un- structured environments requires dynamic path planning, the detection algorithm must also be able to output guidance results. The network described here is able to output results in real time for the input patterns. We have trained the network to recognize common landmarks such as road signs (stop sign, yield sign, pedestrian-crossing sign, etc.). The algorithm has been successfully integrated into a mobile robot testbed developed at the Robotics and In- telligent Systems Laboratory at North Carolina State University.