Place Categorization and Semantic Mapping on a Mobile Robot

Understand

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training.

• Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network.
• We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online.
• Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence.