A Robust Localization System for Inspection Robots in Sewer Networks

Abstract

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

“…The methods presented here have been developed as an augmentation of the typical pose-graph optimization problem so that extension to two or three dimensions and the addition of prior knowledge can be done without significant further alteration of the method, while knowledge of the position of the whole pipe would reduce the problem to one dimension. Other methods of incorporating prior knowledge of the position of the pipe have been demonstrated in a recent work on localization of robots in sewer systems [ 46 ] and in a work on localization of autonomous road vehicles [ 47 , 48 , 49 , 50 ].…”

Autonomous Dam Surveillance Robot System Based on Multi-Sensor Fusion

“…The update step takes into account the network topology and other active methods. The system works well in practical experiments [25]. To address existing positioning systems do not perform well in urban canyons, tunnels, and indoor parking lots for driverless cars, an EKF based multi-sensor positioning system that combines a GPS sensor, a camera, and in-vehicle sensors assisted by kinematic and dynamic vehicle models [26].…”

Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

“…Finally, a precisely calibrated wheel odometry model would have further advantages than the mentioned localization problems, e.g., accurate bias and noise estimation for GNSS and IMU sensors or plausibility check for other quantities. It is clear that the fusion of different localization method needful in an autonomous vehicle [ 11 ], but it is also important that the individual methods perform at its own maximum performance. Therefore, the wheel odometry model has a clear and significant role in the autonomous driving technology, however, a precise and well-calibrated model is difficult to obtain.…”