Generating Synthetic Point Clouds of Sewer Networks: An Initial Investigation

Abstract

Automatic robot inspections of sewer systems are progressively becoming more used for extending the lifetime of sewers and lowering the costs of maintenance. These automatic systems rely on machine learning and the acquisition of varied training data is therefore necessary. Capturing such data can be a costly and time consuming process. This paper proposes a system for generation and acquisition of synthetic training data from sewer systems. The system utilizes Structured Domain Randomization (SDR) for the generation of the sewer systems and an approximated model of a Pico Flexx Time-of-Flight camera for capturing depth and point cloud data from the generated sewer network. We evaluate the proposed system by comparing its output to ground truth data acquired from a Pico Flexx sensor in sewer pipes. We demonstrate that on average our system provides an absolute error of 5.78 ± 8.92 and 7.58 ± 8.68 mm, between data captured from real life and our proposed system, for two different scenarios. These results prove satisfactory for capturing training data. The code is publicly available at https://bitbucket.org/aauvap/syntheticsewerpipes/src/master/.