基于单目相机与激光雷达融合的SLAM方法

SLAM Method Based on Fusion of Monocular Camera and Lidar

视觉传感器与激光雷达融合的SLAM方法是当下研究的热点,实际效果优于单一传感器的SLAM方法。针对当下视觉传感器与激光雷达融合的算法仍然存在着用于定位的特征点不足导致定位精度不高的问题,充分利用激光雷达提供的深度信息,提出了一种多策略的视觉与激光融合的SLAM算法,在估计帧间位姿前对上一帧中的特征点深度值进行判断,通过特征点全部具有深度信息、部分具有深度信息、均不具有深度信息3种判断结果分别采取不同的位姿估计策略。最后在公共数据集KITTI上进行测试,实验结果表明,所提算法有效地提高了定位精度与鲁棒性。

The fusion of visual sensor and lidar is a hotspot of research at present,and its actual effect is superior to that of a single sensor.In existing fusion algorithms of visual sensor and lidar,the feature points for positioning are not sufficient,so that the positioning accuracy is not high enough.To solve the problem,this paper makes full use of the depth information provided by lidar,and proposes a multi-strategy SLAM algorithm based on fusion of vision and laser.Before estimating inter-frame posture,the depth value of the feature points in the last frame is judged.According to the three kinds of judgment results,that is,all the feature points have depth information,part of the feature points have depth information,and none of the feature points has depth information,different pose estimation strategies are adopted respectively.Finally,the algorithm is tested on the public data set KITTI,and the experimental results show that the algorithm effectively improves the positioning accuracy and robustness.