Autonomous Robot Navigation: Using Multiple Semi-supervised Models for Obstacle Detection

This paper proposes a novel approach to efficiently creating multiple semisupervised models of obstacles for life time learning applied to autonomous robot navigation. While previous techniques, which used predefined models of obstacles and terrain, have had success in constrained environments, this paper provides a framework to improve upon this naive technique in obstacle-rich environments.