Compact & Comprehensive Canonical Appearances Discovered Autonomously

This paper presents an exploration approach for discovering canonical appearances in unknown environments using an autonomous ground robot equipped with a depth sensor. This approach is based on the previously proposed two-stage algorithm that alternates between local and global decision making for efficient topological mapping based on bubble space representation. Differing from it, the approach aims to identify vantage viewpoints with characterizing views for subsequent appearance-based learning as well as achieving complete coverage. This is demonstrated by a series of experiments using an outdoor benchmark data set including a comparative study with evaluation metrics including the exploration path length and number of canonical appearances discovered.