基于穿戴式激光头盔的固态激光雷达-惯导三维场景重建方法

A solid-state lidar-inertial 3D scene reconstruction method based on wearable laser helmet

三维地图构建在无人驾驶、资源勘探等领域具有大量的应用.穿戴式激光头盔传感器具有低成本、小型化的特点,可以在GNSS信号缺失或通行性有限的区域实现高精度三维地形重建.然而,穿戴式激光头盔传感器在采集数据过程中存在剧烈抖动和快速转弯等问题,降低了轨迹估计和三维重建结果精度.为了解决这些挑战,首先,本文提出一种基于中间参考点的点云运动畸变补偿方法,降低单帧点云数据运动畸变;然后,本文提出一种基于局部邻域特征一致性判别的误差模型构建方法,提高传感器轨迹估计和三维场景重建精度.最后,本文在设计的穿戴式激光头盔传感器和公开数据集上对本文方法和代表性的固态激光雷达-惯导三维重建方法进行综合对比分析.实验结果表明,本文提出的方法在所有对比方法中取得了最优的轨迹估计和三维重建结果.

3D reconstruction is widely used in autonomous driving,resource exploration,etc.Wearable laser helmet sensor can achieve highprecision 3D reconstruction performance in areas without GNSS signals or limited access,due to its low-cost and miniaturized.However,during the data collection process,the wearable laser helmet sensor is prone to problems,such as severe jitter and rapid turns,which reduce the accuracy of trajectory estimation and 3D reconstruction results.To address these challenges,we first develop a point cloud motion compensation method based on intermediate reference points,which can reduce the motion distortion of point cloud data.Then,to improve the robustness,we propose an error model construction method based on local neighborhood feature consistency discrimination,which further improve the accuracy of sensor trajectory estimation and 3D reconstruction.Finally,we conducte a comprehensive comparative analysis of the method with the representative methods in public data sets,and designe a wearable laser helmet sensor to validate the effectiveness of the proposed method.Experimental results show that the proposed method achieves the best trajectory estimation and 3D reconstruction performance.