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

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 loop closure detection under a multi-factor pose graph of length distance. Meanwhile, Since the algorithm operates within a globally consistent navigation coordinate system, point-based corrections using GNSS/UWB signals or spherical targets can enhance the accuracy of the global map. The proposed method is evaluated and compared to state-of-the-art approaches through simulation tests in a virtual subterranean roadway and field experiments at an active underground coal mine using our own robot platform. These evaluations demonstrate the method's performance and practicability in large-scale underground coal mine environments.