The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless, the typical effective global refinement techniques employed for RGB-D sensors are not widely applied to LiDARs. This letter presents a novel BA photometric strategy that accounts for both RGB-D and LiDAR in the same way. Our work can be used on top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We conducted different experiments using these two depth sensors on public benchmarks. Our results show that our system performs on par or better compared to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association and without making assumptions about the environment. In addition, we present the benefit of jointly using RGB-D and LiDAR within our unified method.

Photometric LiDAR and RGB-D Bundle Adjustment / Di Giammarino, Luca; Giacomini, Emanuele; Brizi, Leonardo; Salem, Omar; Grisetti, Giorgio. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 8:7(2023), pp. 4362-4369. [10.1109/LRA.2023.3281907]

Photometric LiDAR and RGB-D Bundle Adjustment

Di Giammarino, Luca
Primo
;
Giacomini, Emanuele;Brizi, Leonardo;Salem, Omar;Grisetti, Giorgio
2023

Abstract

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless, the typical effective global refinement techniques employed for RGB-D sensors are not widely applied to LiDARs. This letter presents a novel BA photometric strategy that accounts for both RGB-D and LiDAR in the same way. Our work can be used on top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We conducted different experiments using these two depth sensors on public benchmarks. Our results show that our system performs on par or better compared to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association and without making assumptions about the environment. In addition, we present the benefit of jointly using RGB-D and LiDAR within our unified method.
2023
slam; mapping; range sensors;
01 Pubblicazione su rivista::01a Articolo in rivista
Photometric LiDAR and RGB-D Bundle Adjustment / Di Giammarino, Luca; Giacomini, Emanuele; Brizi, Leonardo; Salem, Omar; Grisetti, Giorgio. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 8:7(2023), pp. 4362-4369. [10.1109/LRA.2023.3281907]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1684477
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