This paper proposes a novel visual simultaneous localization and mapping (SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), that employs a panoramic camera and a tilted multi-beam LiDAR scanner to generate accurate and metrically-scaled trajectories. RGB-D SLAM was the design basis for HDPV-SLAM, which added depth information to visual features. It aims to solve the two major issues hindering the performance of similar SLAM systems. The first obstacle is the sparseness of LiDAR depth, which makes it difficult to correlate it with the extracted visual features of the RGB image. A deep learning-based depth estimation module for iteratively densifying sparse LiDAR depth was suggested to address this issue. The second issue pertains to the difficulties in depth association caused by a lack of horizontal overlap between the panoramic camera and the tilted LiDAR sensor. To surmount this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature-based triangulation and depth estimation. During a phase of feature tracking, this hybrid depth association module aims to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the deep learning-based corrected depth. We evaluated the efficacy of HDPV-SLAM using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. The experimental results demonstrate that the two proposed modules contribute substantially to the performance of HDPV-SLAM, which surpasses that of the state-of-the-art (SOTA) SLAM systems.

翻译：本文提出一种名为混合深度增强全景视觉SLAM（HDPV-SLAM）的新型视觉同时定位与地图构建系统，该系统采用全景相机与倾斜多波束激光雷达扫描仪生成精确且具有公制尺度的轨迹。HDPV-SLAM以RGB-D SLAM为设计基础，为视觉特征添加深度信息，旨在解决制约同类SLAM系统性能的两大问题。首要障碍是激光雷达深度稀疏性导致难以与RGB图像提取的视觉特征关联，为此提出基于深度学习的深度估计模块迭代式稠密化稀疏激光雷达深度。第二个问题涉及全景相机与倾斜激光雷达传感器因缺乏水平重叠区域而导致的深度关联困难，我们提出混合深度关联模块，通过优化组合基于特征三角化与深度估计两种独立流程的深度信息。该模块在特征跟踪阶段旨在最大化利用跟踪视觉特征三角化深度与深度学习修正深度中更精确的深度信息。我们采用18.95公里长的约克大学与Teledyne Optech（YUTO）移动测绘数据集评估HDPV-SLAM性能，实验结果表明，两个提出模块对HDPV-SLAM性能提升显著，其综合表现超越现有最优SLAM系统。