ImMesh: An Immediate LiDAR Localization and Meshing Framework

Lin, Jing; Yuan, Chongjiang; Cai, Yixi; Li, Haotian; Zou, Yuying; Hong, Xiaoping; Zhang, Fu

doi:10.48550/arxiv.2301.05206

Cited by 2 publications

(3 citation statements)

References 58 publications

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“…In recent years, some methods have been incorporating colour information into point clouds during the mapping process, such as refs. [4,[11][12][13]. These methods require precise offline extrinsic calibrations between the LiDAR and cameras, as well as strict hardware time synchronisation, which leads to increased costs.…”

Section: Mapping Solutionsmentioning

confidence: 99%

“…Alternatively, some LiDAR-visual systems are employed to acquire a coloured map during localisation, as demonstrated in previous works such as refs. [4,[11][12][13]. However, these methods require precise offline extrinsic calibration between the LiDAR and cameras, along with strict hardware time synchronisation, which leads to increased costs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

Jiang,

Wan,

Gao

et al. 2024

IET Cyber-Syst and Robotics

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The colour‐enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER‐Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER‐Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold‐based residual selection in LiDAR‐inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

show abstract

Section: Mapping Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

Jiang,

Wan,

Gao

et al. 2024

IET Cyber-Syst and Robotics

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show abstract

“…However, Puma utilizes ray-casting for registration and needs to rebuild the mesh map repetitively via the Poisson reconstruction [26] rather than updating it. Lin et al [27] also investigated online meshing in LiDAR SLAM. They use a separate SLAM system and a direct connecting meshing strategy upon point-cloud maps.…”

Section: Related Workmentioning

confidence: 99%

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

Ruan¹,

Li²,

Wang³

et al. 2023

Preprint

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Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become an attractive dense model for mapping in recent years. However, existing methods usually produce mesh maps by using an offline post-processing step to generate mesh maps. This two-step pipeline does not allow these methods to use the built mesh maps online and to enable localization and meshing to benefit each other. To solve this problem, we propose the first CPU-only real-time LiDAR SLAM system that can simultaneously build a mesh map and perform localization against the mesh map. A novel and direct meshing strategy with Gaussian process reconstruction realizes the fast building, registration, and updating of mesh maps. We perform experiments on several public datasets. The results show that our SLAM system can run at around 40Hz. The localization and meshing accuracy also outperforms the state-of-the-art methods, including the TSDF map and Poisson reconstruction. Our code and video demos are available at: https://github.com/lab-sun/SLAMesh.

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ImMesh: An Immediate LiDAR Localization and Meshing Framework

Cited by 2 publications

References 58 publications

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

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