LIVER: A Tightly Coupled LiDAR-Inertial-Visual State Estimator With High Robustness for Underground Environments

In this letter, we propose a tightly coupled LiDAR-inertial-visual (LIV) state estimator termed LIVER, which achieves robust and accurate localization and mapping in underground environments. LIVER starts with an effective strategy for LIV synchronization. A robust initialization process that integrates LiDAR, vision, and IMU is realized. A tightly coupled, nonlinear optimization-based method achieves highly accurate LiDAR-inertial-visual odometry (LIVO) by fusing LiDAR, visual, and IMU information. We consider scenarios in underground environments that are unfriendly to LiDAR and cameras. A visual-IMU-assisted method enables the evaluation and handling of LiDAR degeneracy. A deep neural network is introduced to eliminate the impact of poor lighting conditions on images. We verify the performance of the proposed method by comparing it with the state-of-the-art methods through public datasets and real-world experiments, including underground mines. In underground mines test, tightly coupled methods without degeneracy handling lead to failure due to self-similar areas (affecting LiDAR) and poor lighting conditions (affecting vision). In these conditions, our degeneracy handling approach successfully eliminates the impact of disturbances on the system.