Learning from depth sensor data using inductive logic programming

Abstract

The problem of detecting objects and their movements in sensor data is of crucial importance in providing safe navigation through both indoor and outdoor environments for the visually impaired. In our setting we use depth-sensor data obtained from a simulator and use inductive logic programming (ILP), a subfield of machine learning that deals with learning concept descriptions, to learn how to detect borders, find the border that is nearest to some point of interest, and border correspondence through time. We demonstrate how ILP can be used to tackle this problem in an incremental manner by using previously learned predicates to construct more complex ones. The learned concept descriptions show high (> 90%) accuracy and their natural language interpretation closely matches an intuitive understanding of their meaning.