Visual object detection for autonomous sewer robots

Abstract

The goal of the proposed detection system is to identify objects, e.g. inlets, in sewage pipes. A camera attached to an autonomous sewer robot provides images that are interpreted by an attention driven recognition module. Local appearances in the input image are represented in an environment specific description subspace extracted by principal component analysis. The object class posterior interpretation in terms of a radial basis function network constitutes an attention filter constraining further processing on receptive fields filter resolutions. Multiresolution decision fusion is the framework used to combine detection confidences to enhance robustness in the global classification. The vision system is evaluated in various experiments where it proves successful with respect to the local classification rate, to the generalization behavior in recognizing similar objects, and to detection that requires a minimum of positive falses.