A SLAM Method Based on Multi-Robot Cooperation for Pipeline Environments Underground

Abstract

SLAM (simultaneous localization and mapping) technology has recently shown considerable forward progress; however, most of the mainstream SLAM technologies are currently based on laser- and vision-based fusion strategies. However, there are problems (e.g., a lack of geometric structure, no significant feature points in the surrounding environment, LiDAR degradation, and the longitudinal loss of constraints, as well as missing GPS signals within the pipeline) in special circumstances (e.g., in underground pipelines and tunnels), thus making it difficult to apply laser or vision SLAM techniques. To solve this issue, a multi-robot cooperation-based SLAM method is proposed in this study for pipeline environments, based on LIO-SAM. The proposed method can effectively perform SLAM tasks in spaces with high environmental similarity (e.g., tunnels), thus overcoming the limitation that existing SLAM methods have been poorly applied in pipeline environments due to the high environmental similarity. In this study, the laser-matching part of the LIO-SAM is removed, and a high-precision differential odometer, IMU inertial navigation sensor, and an ultrasonic sensor, which are not easily affected by the high similarity of the environment, are employed as the major sources of positioning information. Moreover, a front-to-back queue of two robots is trained in the pipeline environment; a unique period-creep method has been designed as a cooperation strategy between the two robots, and a multi-robot pose constraint factor (ultrasonic range factor) is built to constrain the robots’ relative poses. On that basis, the robot queue can provide a mutual reference when traveling through the pipeline and fulfill its pose correction with high quality, thus achieving high positioning accuracy. To validate the method presented in this study, four experiments were designed, and SLAM testing was performed in common environments, as well as simple and complex urban pipeline environments. Next, error analysis was conducted using EVO. The experimental results suggest that the method proposed in this study is less susceptible to environmental effects than the existing methods due to the benefits of multi-robot cooperation. This applies to a common environment (e.g., a room) and can achieve a good performance; this means that a wide variety of piping environments can be established with high similarity. The average error of SLAM in the pipeline was 0.047 m, and the maximum error was 0.713 m, such that the proposed method shows the advantages of controllable cumulative error, high reliability, and robustness with an increase in the scale of the pipeline and with an extension of the operation time.

Tripartite Social Roles of Urban Underground Pipeline Informatization in China

“…The informatization of UUPs is a crucial component of digital city development, offering significant economic and social benefits. It can reduce pipeline accidents, lower leakage rates, and prevent duplicate construction [30]. Data-driven planning and management using multi-source data are key components of UUP informatization.…”

Development and Verification of an Autonomous and Controllable Mobile Robot Platform

“…The basic principle of relative positioning is to calculate the pose change between adjacent moments through the internal sensor information on the premise that the initial pose of the mobile robot is given, realizing the realtime estimation of the pose. This method is also called dead reckoning, which predicts the trajectory of the robot based on the kinematics model [17]. The internal sensors commonly used in relative positioning methods mainly include odometers, gyroscopes, and inertial sensors.…”

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

“…The collaborative navigation method is a feasible solution. Through the collaboration constraints measured by multi-robots, the state can be updated based on pose constraint factors and achieve high positioning accuracy [19]. Regarding favorable environment estimation, Chen et al constructed a prior map by LiDAR to assist in estimating an inertial/GNSS tight-coupled navigation system when GNSS is in good condition.…”