Research on multimodal data enhanced SLAM algorithm for global mapping of underground coal mines.

ABSTRACT: To resolve the issue of inaccurate global mapping in coal mines, which impedes reliable support for autonomous driving, the paper proposes an enhanced SLAM global mapping method for coal mines based on multimodal data, which consists of LiDAR-inertial-wheel odometry frontend and a multi-factor motivated optimization backend, to realize the pose estimation and map establishment for global mapping of underground roadways. The frontend fuses inertial measurements from the M-SINS, which are compensated for the Earth rotation, with wheel encoder data and LiDAR points using an iterated error-state Kalman filter, mitigating the LiDAR degeneration. The backend utilizes a series of identified spherical targets, sparsely deployed in the tunnel, to introduce global position constrains and unambiguous

SUBMITTER: Hao M 

PROVIDER: S-EPMC12549982 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature