基于多传感信息融合的语义词袋SLAM优化算法

针对室外大范围场景移动机器人建图中，激光雷达里程计位姿计算不准确导致SLAM (simultaneous localization and mapping)算法精度下降的问题，提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)。采用视觉惯性系统引入激光雷达原始观测数据，并通过滑动窗口实现了IMU (inertia measurement unit)量测、视觉特征和激光点云特征的多源数据联合非线性优化；最后算法利用视觉与激光雷达的语义词袋互补特性进行闭环优化，进一步提升了多传感器融合SLAM系统的全局定位和建图精度。实验结果显示，相比于传统的紧耦合双目视觉惯性里程计和激光雷达里程计定位，MSW-SLAM算法能够有效探测轨迹中的闭环信息，并实现高精度的全局位姿图优化，闭环检测后的点云地图具有良好的分辨率和全局一致性。

Multi-sensor information fusion SLAM based on semantic word bags

This paper proposed an algorithm known as MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags) to address the issue of inaccurate LiDAR odometry position and pose calculations in the mapping of outdoor large-scale environments by mobile robots, resulting in a decrease in the accuracy of the simultaneous localization and mapping(SLAM) algorithm. This algorithm incorporated raw observation data from LiDAR using a visual inertial system and conducts joint nonlinear optimization of measurements from the inertial measurement unit(IMU), visual features, and laser point cloud features using sliding windows, the algorithm leveraged the complementary semantic word bag characteristics of vision and LiDAR for closed-loop optimization, further enhancing the global positioning and mapping accuracy of the multi-sensor fusion SLAM system. Experimental results demonstrate that, compared to traditional tightly coupled binocular vision-inertial odometry and LiDAR odometry positioning, the MSW-SLAM algorithm can effectively detect closed-loop information in trajectories and achieve high-precision global pose optimization. The point cloud map after closed-loop detection exhibits excellent resolution and global consistency.