Legged robot-aided 3D tunnel mapping via residual compensation and anomaly detection

Abstract

Three-dimensional (3D) mapping is important to achieve early warning for construction safety and support the long-term safety maintenance of tunnels. However, generating 3D point cloud maps of excavation tunnels that tend to be deficient in features, have rough lining structures, and suffer from dynamic construction interference, can be a challenging task. In this paper, we propose a novel legged robot-aided 3D tunnel mapping method to address the influence of point clouds in the mapping phase. First, a method of kinematic model construction that integrates information from both the robot’s motors and the inertial measurement unit (IMU) is proposed to correct the motion distortion of point clouds. Then, a residual compensation model for unreliable regions (abbreviated as the URC model) is proposed to eliminate the inherent alignment errors in the 3D structures. The structural regions of a tunnel are divided into different reliabilities using the K-means method, and an inherent alignment metric is compensated based on region residual estimation. The compensated alignment metric is then incorporated into a rotation-guided anomaly consistency detection (RAD) model. An isolation forest-based anomaly consistency indicator is designed to remove anomalous light detection and ranging (LiDAR) points and reduce sensor noise caused by ultralong distances. To verify the proposed method, we conduct numerous experiments in three tunnels, namely, a drilling and blasting tunnel, a TBM tunnel, and an underground pedestrian tunnel. According to the experimental results, the proposed method achieves 0.84 ‰, 0.40 ‰, and 0.31 ‰ closure errors (CEs) for the three tunnels, respectively, and the absolute map error (AME) and relative map error (RME) are approximately 1.45 cm and 0.57 %, respectively. The trajectory estimation and mapping errors of our method are smaller than those of existing methods, such as FAST-LIO2, Faster-LIO and LiLi-OM. In addition, ablation tests are conducted to further reveal the roles of the different models used in our method for legged robot-aided 3D mapping in tunnels.