Stacked Object Clustering with Adaptive Scanning and Density Centralized Voting
Publication: Computing in Civil Engineering 2023
ABSTRACT
Vision-based sensors have been widely used in reality capture and the corresponding scene understanding tasks such as object detection. Given the increasing complexity of built environments, geometric features from the raw scanning data can become too vague for effective object detection. One example challenge is stacked object recognition. Previous methods propose to use high-resolution sensors to capture more detailed geometry information to highlight the boundaries between adjacent objects, which increases the deployment cost and computing needs. This paper proposes a novel data augmentation and voting method for stacked object detection with only low-cost sparse LiDAR sensors. First, a modified Lidar Odometry and Mapping (LOAM) method is used to register and augment raw point cloud data from multiple scans in real time. Then a voxel-based density voting method is applied to centralize the points in an enhanced scan for more accurate clustering. Finally, the clustered points are grouped and applied to generate 3D bounding boxes for object boundary identification. A pilot test was performed to show the improved results of the proposed methods for both reality capture and clustering.
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Computing in Civil Engineering 2023
Pages: 317 - 325
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Adaptive systems
- Benefit cost ratios
- Buildings
- Business management
- Computer vision and image processing
- Computing in civil engineering
- Design (by type)
- Domain boundary
- Engineering fundamentals
- Equipment and machinery
- Financial management
- Geometrics
- Highway and road design
- Mathematics
- Methodology (by type)
- Practice and Profession
- Probe instruments
- Structural engineering
- Structures (by type)
- Systems engineering
- Systems management
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