ABSTRACT

Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method.

摘要

准确高效的三维（3D）街景重建是勘探工具安全导航并执行高级任务的基本能力。近来，在通过视觉图像和光检测和测距（LiDAR）进行的街景重建中已取得了显着进展，但是它们在大规模户外缩放和重建或高效处理方面都存在困难。为了解决这些问题，本文提出了一种自动方法，用于近实时从粗糙到精细的大规模3D街景的增量密集重建。首先，通过视觉和激光测距法（VLO）估计车辆的姿态，并引入最先进的金字塔立体匹配网络（PSMNet）来估计深度信息。然后，通过关键帧选择和带有局部优化的粗注册来进行增量密集3D街景重建。最后，多余的点和噪声点通过多次滤波被去除，从而产生了高质量的密集重构。进行了全面的实验，以检查视觉效果，轨迹姿态误差和多尺度模型，以基于最新方法提供的参考轨迹和重构，对云比较（M3C2）进行建模，显示了精度，召回率和F抽样核心点的SCP-得分分别超过80.42％，71.68％和77.19％，证明了该方法的有效性。