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Smart city article details
| Title | Dense Three-Dimensional Color Reconstruction For Large-Scale Outdoor Scenes |
|---|---|
| ID_Doc | 18325 |
| Authors | Liu Z.; Guo S.; Pun M.-O. |
| Year | 2023 |
| Published | 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 |
| DOI | http://dx.doi.org/10.1109/APSIPAASC58517.2023.10317418 |
| Abstract | Accurate three-dimensional (3D) color models play a crucial role in various emerging applications, such as geographic surveying and mapping, smart cities, and digital twin cities. To achieve high-fidelity representations of large-scale scenes, recent advancements in machine learning techniques and LiDAR-based Simultaneous Localization and Mapping (SLAM) have been instrumental. Leveraging these cutting-edge technologies, this paper presents a sensor-fusion framework that integrates a solid-state LiDAR, an inertial measurement unit (IMU), and a monocular camera. Additionally, a point cloud upsampling technique is introduced into LiDAR SLAM to enhance the density of the point clouds. Within this framework, the LiDAR-IMU odometry accurately estimates the positions and poses of the collected point clouds. Simultaneously, synchronized images are captured and utilized to incorporate texture information into the point clouds. The proposed framework demonstrates its capability to generate highly detailed and dense 3D color models for large-scale outdoor scenes, all within a limited on-site scanning time. Extensive experimental results validate the effectiveness and efficiency of the proposed approach. © 2023 IEEE. |
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