APGC-LVIW: LiDAR-visual-inertial-wheel SLAM with adaptive planar geometric constraints

This paper proposes a novel multisensor fusion ground SLAM system that integrates LiDAR, visual, IMU, and wheel encoder data to enhance localization and mapping accuracy in complex environments. The proposed system consists of a LiDAR subsystem and a visual subsystem. To fully leverage the complementary advantages of LiDAR and visual sensors, the two subsystems provide each other with initial values for optimization. LiDAR supplies high-precision depth information to the visual subsystem, while the visual subsystem assists the LiDAR module in motion distortion correction, thereby enhancing the overall accuracy of state estimation. In addition, a novel plane extraction method is proposed based on adaptive dynamic distance thresholds and normal vector consistency verification. And the planar factor is introduced into the LiDAR subsystem for joint optimization. Constraining continuous planar structures (such as the ground and walls) reduces matching ambiguity, which further decreases local map noise and improves mapping accuracy. The proposed method has been extensively evaluated in complex urban campus scenarios, and the results show the effectiveness and accuracy of the algorithm.