Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

PVL-Cartographer: Panoramic Vision-aided LiDAR Cartographer-based SLAM for Maverick Mobile Mapping System

Version 1 : Received: 18 March 2023 / Approved: 20 March 2023 / Online: 20 March 2023 (04:00:38 CET)

A peer-reviewed article of this Preprint also exists.

Zhang, Y.; Kang, J.; Sohn, G. PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System. Remote Sensing 2023, 15, 3383, doi:10.3390/rs15133383. Zhang, Y.; Kang, J.; Sohn, G. PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System. Remote Sensing 2023, 15, 3383, doi:10.3390/rs15133383.

Abstract

Mobile Mapping System (MMS) plays a crucial role in generating high-precision 3D maps for various applications. However, the traditional MMS that uses tilted LiDAR (light detection and ranging) has limitations in capturing complete information of the environment. To overcome these limitations, we propose a panoramic vision-aided Cartographer simultaneous localization and mapping (SLAM) system for MMS, named "PVL-Cartographer". The proposed system integrates multiple sensors to achieve accurate and robust localization and mapping. It contains two sub-systems, early fusion and middle fusion. In the early fusion, range-maps are created from LiDAR points in a panoramic image space, facilitating the incorporation of visual features. The SLAM system works with both visual features with and without augmented ranges. In the middle fusion, a pose graph combines camera and LiDAR nodes, with IMU (Inertial Measurement Unit) data providing constraints between each node. Extensive experiments in challenging outdoor scenarios demonstrate the effectiveness of the proposed SLAM system in producing accurate results, even in conditions with limited features. Overall, our proposed PVL Cartographer system offers a robust and accurate solution for MMS localization and mapping.

Keywords

SLAM, localization, mapping, mobile mapping system, spherical camera, panoramic image, LiDAR, IMU, sensor fusion, pose graph.

Subject

Computer Science and Mathematics, Logic

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