Approaching real-world navigation using object recognition network

Typical navigation systems do not use object recognition as part of their autonomous driving systems. People often use hand-crafted features (e.g. lanes, traffic lights, intersections) based on programmers´ knowledge about the environment. Those agents are usually brittle during real-world tests. However, landmarks, as a type of object, need to be recognized for an autonomous navigation system to generalize its learned training data to other unfamiliar environments. In this work we utilize the Developmental Network (DN), which has been tested extensively with object recognition tasks, for a mobile agent and train it to self-navigate in controlled indoor environment. The proposed system uses Lobe Component Analysis (LCA) to learn features from both stereo cameras and desired navigation actions. Neurons attend to different areas of the input image. They compete for firing according to the goodness of matching result. This enables the agent to attend to image local patches as landmarks without explicitly defining objects. Our analysis shows that attention can be corrected by direct supervision and by indirect reinforcement provided by the teacher. We anticipate our work in this paper to be a starting point of research efforts that shift expensive range-scanner-based methods to inexpensive camera-based methods that, although using richer information, face challenges of object appearance variations.