基于稀疏激光点云数据和单帧图像融合的三维重构算法

由于激光雷达获取的深度数据非常稀疏,为了能够将深度数据与图像数据重构出稠密三维深度图,本文提出了基于稀疏激光点云数据和单帧图像融合的三维重构算法。该方法首先使用点直方图特征有效地选择对应于目标的点数据并消除体素中的非相似点;然后,使用高斯过程回归对局部深度数据建模,并通过插值获得三维深度数据,本文算法获得的三维深度点更接近基准值,并保持了目标的局部形状特征;最后,利用马尔科夫随机场对图像灰度数据和三维插值点进行融合来构建三维深度图。仿真实验结果表明:相比现有基于激光雷达数据和单目图像数据的三维重建算法,本文提出的算法将大大提升算法的鲁棒性与重构的准确度,可辅助用于复杂的城市场景中车辆的无人驾驶。

3D Reconstruction Algorithm Based on Sparse Laser Point-cloud and Single-frame Image

Since the depth data acquired by LIDAR is very sparse, a three-dimensional reconstruction algorithm based on sparse laser point cloud data and single frame image is proposed so as to reconstruct the 3D depth map from the depth data and image data in this paper. The proposed algorithm firstly uses the point histogram feature to effectively select the point data corresponding to the target and eliminate the non-similar points in the voxels. Then, the local depth data is modeled by Gaussian process regression, and the 3D depth data is obtained by interpolation. The 3D depth points obtained by our algorithm are closer to the reference value and keep the local shape feature of the object. Compared with existing 3D reconstruction algorithms based on LIDAR data and image data, simulation results show that the algorithm proposed in this paper will greatly enhance the robustness and reconstruction accuracy, and can be used for the unmanned vehicle in complicated urban scenes.