为解决无人驾驶汽车在复杂动态场景下单一传感器感知局限及运动控制耦合性强的问题,文章开展基于SLAM的环境感知与控制系统研究。设计多传感器融合的SLAM架构,通过激光雷达、相机与IMU紧耦合实现鲁棒状态估计与高精度地图构建,引入点云去噪及时序同步机制提升感知品质;提出分层路径规划策略,并设计基于模型预测控制的横纵向协同运动控制器,充分考虑车辆动力学约束与道路边界。实车测试表明,系统在复杂场景下定位精度达厘米级,横向跟踪误差≤18.3cm,可为无人驾驶自主导航提供可靠技术支撑。

To address the limitations of single sensor perception and the strong coupling of motion control in unmanned vehicles in complex dynamic scenarios, this paper conducts research on an environmental perception and control system based on SLAM. Design a multi-sensor fusion SLAM architecture, achieve robust state estimation and high-precision map construction through the tight coupling of lidar, camera and IMU, and introduce point cloud denoising and time-sequence synchronization mechanisms to improve perception quality. A hierarchical path planning strategy is proposed, and a transverse and longitudinal cooperative motion controller based on model predictive control is designed, fully considering the vehicle dynamic constraints and road boundaries. Real vehicle tests show that the system achieves centimeter-level positioning accuracy in complex scenarios, with a lateral tracking error of no more than 18.3cm, which can provide reliable technical support for autonomous navigation of unmanned vehicles.