Visual simultaneous localisation and mapping for sewer pipe networks leveraging cylindrical regularity

This work proposes a novel visual Simultaneous Localisation and Mapping (vSLAM) approach for robots in sewer pipe networks. One problem of vSLAM in pipes is that the scale drifts and accuracy degrades. We propose the use of structural information to mitigate this problem via cylindrical regularity. The main novelty consists of an approach for cylinder detection that is more robust than previous methods in non-smooth sewer pipe environments. Cylindrical regularity is then incorporated into both local bundle adjustment and pose graph optimisation. The approach adopts a minimal cylinder representation with only five parameters, avoiding constraints during the optimisation in vSLAM. A further novelty is that the estimated cylinder is part of the scale drift estimation, which enables a correction to the translation estimate and this further improves the accuracy. The approach, termed Cylindrical Regularity ORB-SLAM (CRORB), is benchmarked and compared to leading visual SLAM algorithms ORB-SLAM2 and direct sparse odometry (DSO), as well as a vSLAM algorithm with cylindrical regularity developed for gas pipes, using real sewer pipe data and synthetic data generated with the Gazebo modelling software. The results demonstrate that CRORB improves substantially over the competitors, with a reduction of approximately 70% in error on real data.