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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 togenerate accurate and metrically-scaled trajectories. RGB-D SLAM was the designbasis for HDPV-SLAM, which added depth information to visual features. It aimsto solve the two major issues hindering the performance of similar SLAMsystems. The first obstacle is the sparseness of LiDAR depth, which makes itdifficult to correlate it with the extracted visual features of the RGB image.A deep learning-based depth estimation module for iteratively densifying sparseLiDAR depth was suggested to address this issue. The second issue pertains tothe difficulties in depth association caused by a lack of horizontal overlapbetween the panoramic camera and the tilted LiDAR sensor. To surmount thisdifficulty, we present a hybrid depth association module that optimallycombines depth information estimated by two independent procedures,feature-based triangulation and depth estimation. During a phase of featuretracking, this hybrid depth association module aims to maximize the use of moreaccurate depth information between the triangulated depth with visual featurestracked and the deep learning-based corrected depth. We evaluated the efficacyof HDPV-SLAM using the 18.95 km-long York University and Teledyne Optech (YUTO)MMS dataset. The experimental results demonstrate that the two proposed modulescontribute substantially to the performance of HDPV-SLAM, which surpasses thatof the state-of-the-art (SOTA) SLAM systems.

HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera