Abstract
Accurate position estimation is critical for the reliable navigation of autonomous mobile robots (AMRs) in indoor construction environments. Conventional methods, including landmark detection using rangefinders, visionbased optical sensors, and global positioning system (GPS)- based localization, encounter limitations in large, clutterfree indoor industrial spaces. Similarly, light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) requires premap construction, which is time-consuming and impractical for dynamic construction sites. To address these challenges, this article proposes a planar marker-based position estimation system that enables immediate deployment without premapping, optimized for multirobot collaboration in indoor construction environments. The proposed system employs 3-D marker recognition with minimal setup, using markers placed on both the robot and a designated home position. The interrobot communication enables relative position estimation and coordinate sharing, while accumulated odometry errors are periodically reset using the home marker to minimize positional drift. The experimental validation demonstrates position errors below 100 mm over a 20-m travel distance, with standard deviations of ±2.5 and ±2.0 mm in the X- and Y-axes, respectively, and an angular error of ±0.1° during docking. These results confirm that the proposed method achieves accurate trajectory tracking and rapid environmental adaptability, significantly enhancing the efficiency and robustness of collaborative AMR operations in large-scale indoor construction sites.
| Original language | English |
|---|---|
| Pages (from-to) | 2978-2990 |
| Number of pages | 13 |
| Journal | IEEE Sensors Journal |
| Volume | 26 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2026 |
Bibliographical note
Publisher Copyright:© 2001-2012 IEEE.
Keywords
- Autonomous mobile robots (AMRs)
- construction robotics
- indoor navigation
- multi-robot cooperation
- multiple serial makers
- planar marker recognition
- robot localization
- service robot
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