Autonomous navigation through case-based learning

Abstract

This paper presents an unconventional approach to vision-guided autonomous navigation. The system recalls information about scenes and navigational experience using content-based retrieval from a visual database. To achieve a high applicability and adaptability to various road types, we do not impose a priori scene features, such as road edges, that the system must use, but rather the system automatically selects features from images during supervised learning. A new self-organizing scheme called recursive partition tree (RPT) is used for automatic construction of a vision-and-control database, which quickly prunes the data set in the content-based search and results in a low time complexity of log(n) for retrieval from a database of size n. Experimental results are reported in both indoor and outdoor navigation.