Yan Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yan Sun has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bohan Shi
Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
2 shared publications - Chenyu Shen
Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
2 shared publications - Lei Sun
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
2 shared publications - Siyang Sun
Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
2 shared publications - Wanbiao Lin
Shenzhen Research Institute, Nankai University, Shenzhen 518081, China
2 shared publications - Wenlan Ouyang
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
2 shared publications
Latest Publications
- Open Access
BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.
Open AccessBA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.
- Open Access
- 29 July 2024
- Published by MDPI AG in Preprints.org
BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on BA Cost Factors
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrated that the proposed method achieves accurate trajectory estimation and consistent mapping.
- 29 July 2024
- Published by MDPI AG in Preprints.org
Open AccessBA-CLM: A Globally Consistent 3D LiDAR Mapping Based on BA Cost Factors
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrated that the proposed method achieves accurate trajectory estimation and consistent mapping.