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.

翻译：本文提出一种新型视觉同时定位与地图构建（SLAM）方法——混合深度增强全景视觉SLAM（HDPV-SLAM），利用全景相机与倾斜多波束激光扫描仪生成精确且具有度量尺度的车辆轨迹。HDPV-SLAM以RGB-D SLAM为设计基础，为视觉特征添加深度信息，旨在克服限制RGB-D SLAM系统性能的两大问题。第一重障碍在于激光雷达深度稀疏性，这使其难以与RGB图像提取的视觉特征关联。我们通过提出基于深度学习（DL）的稀疏激光雷达深度迭代致密化模块来解决该问题。第二重障碍涉及全景相机与倾斜激光雷达传感器之间水平重叠覆盖显著不足所导致的深度关联挑战。为攻克这一难题，我们提出混合深度关联模块，该模块通过特征三角化与深度估计两种独立流程，对各自估算的深度信息进行最优融合。在特征跟踪阶段，此混合深度关联模块旨在最大化利用视觉特征跟踪所得三角化深度与基于深度学习的校正深度之间更精确的深度信息。我们利用长达18.95公里的约克大学与Teledyne Optech（YUTO）移动测图数据集评估HDPV-SLAM性能。实验结果表明，所提出的两个模块显著提升了HDPV-SLAM的性能，使其优于当前最先进的（SOTA）SLAM系统。