HDPV-SLAM：面向倾斜激光雷达与全景视觉相机的移动测绘系统的混合深度增强全景视觉SLAM
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera
AI总结：
提出HDPV-SLAM，融合全景相机与倾斜激光雷达，通过深度学习稠密化深度和混合深度关联解决稀疏与重叠不足，在YUTO数据集上超越现有SLAM系统。
摘要：
本文提出了一种新颖的视觉同步定位与建图（SLAM）系统，称为混合深度增强全景视觉SLAM（HDPV-SLAM），该系统采用全景相机和倾斜多波束激光雷达扫描仪，以生成准确且具有度量尺度的轨迹。RGB-D SLAM是HDPV-SLAM的设计基础，它向视觉特征添加了深度信息。它旨在解决阻碍类似SLAM系统性能的两个主要问题。第一个障碍是激光雷达深度的稀疏性，这使得难以将其与RGB图像中提取的视觉特征相关联。为此，提出了一种基于深度学习的深度估计模块，用于迭代地稠化稀疏的激光雷达深度。第二个问题涉及由于全景相机与倾斜激光雷达传感器之间缺乏水平重叠而导致的深度关联困难。为了克服这一困难，我们提出了一种混合深度关联模块，该模块最优地结合了由两种独立过程（基于特征的三角测量和深度估计）估计的深度信息。在特征跟踪阶段，该混合深度关联模块旨在最大化利用视觉特征跟踪的三角测量深度与基于深度学习的校正深度之间更准确的深度信息。我们使用18.95公里长的约克大学和Teledyne Optech（YUTO）移动测绘系统数据集评估了HDPV-SLAM的有效性。实验结果表明，所提出的两个模块对HDPV-SLAM的性能有显著贡献，其性能超过了最先进的（SOTA）SLAM系统。
英文摘要：
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.
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