Abstract
This paper proposes a novel visual simultaneous localization and mapping (SLAM), called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), generating accurate and metrically scaled vehicle trajectories using a panoramic camera and a titled multi-beam LiDAR scanner. RGB-D SLAM served as the design foundation for HDPV-SLAM, adding depth information to visual features. It seeks to overcome the two problems that limit the performance of RGB-D SLAM systems. The first barrier is the sparseness of LiDAR depth, which makes it challenging to connect it with visual features extracted from the RGB image. We address this issue by proposing a depth estimation module for iteratively densifying sparse LiDAR depth based on deep learning (DL). The second issue relates to the challenges in the depth association caused by a significant deficiency of horizontal overlapping coverage between the panoramic camera and the tilted LiDAR sensor. To overcome this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature triangulation and depth estimation. This hybrid depth association module intends to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the DL-based corrected depth during a phase of feature tracking. We assessed HDPV-SLAM's performance using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. Experimental results demonstrate that the proposed two modules significantly contribute to HDPV-SLAM's performance, which outperforms the state-of-the-art (SOTA) SLAM systems.
Abstract (translated)
本论文提出了一种新颖的视觉同时定位和地图生成(SLAM)技术,称为混合深度增强全景视觉SLAM(HDPV-SLAM),通过全景相机和带标题的多光束LiDAR扫描器,生成准确且度量尺度化的汽车轨迹。RGB-D SLAM作为HDPV-SLAM的设计基础,添加了深度信息到视觉特征。它旨在克服RGB-D SLAM系统性能限制的两个问题。第一个障碍是LiDAR深度的稀疏性,这使得它与从RGB图像中提取的视觉特征难以连接。我们提出了一种基于深度学习(DL)的深度估计模块,以迭代地增加稀疏的LiDAR深度。第二个问题是由于全景相机和倾斜LiDAR传感器之间水平覆盖缺陷的重大不足,引起的深度匹配挑战。为了克服这个问题,我们提出了一种混合深度匹配模块,最佳地结合了特征三角化和深度估计的两个独立程序得出的信息。该混合深度匹配模块旨在最大限度地利用跟踪特征和基于深度学习纠正的深度在特征跟踪阶段期间使用的信息。我们使用 York University 和 Teledyne Optech(YUTO)的18.95千米长MMS数据集评估了HDPV-SLAM的性能。实验结果显示,提出的两个模块对HDPV-SLAM的性能做出了重要贡献,它比最先进的SLAM系统表现更好。
URL
https://arxiv.org/abs/2301.11823
