  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.
