Long-Term learning using multiple models for outdoor autonomous robot navigation

Abstract

Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research. The navigation task requires identifying safe, traversable paths which allow the robot to progress toward a goal while avoiding obstacles. One approach is to apply Machine Learning techniques that accomplish near to far learning by augmenting near-field Stereo to identify safe terrain and obstacles in the far field. Some mechanism for applying past learned experience to the active navigation task is crucial for effective far-field classification. We introduce a new method for long-term learning in the robot navigation task by selecting a subset of previously learned linear binary classifiers. We then combine their output to produce a final classification for a new image. Techniques for efficient selection of models, as well as the combination of their output, are addressed. We evaluate the performance of our technique on three fully labeled datasets, and show that our technique outperforms several baseline techniques that do not leverage past experience.