An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot
Abstract
:1. Introduction
- An artificial landmark detection approach based on laser reflection intensity is proposed, and a trilateral localization algorithm is developed using the detection and identification results.
- The EKF-based multi-sensor fusion technique is adopted to achieve the integration of trilateral localization results and inertial sensor positioning results.
- The accuracy and practicality of the algorithm are verified in simulation and real environments, respectively. The experimental results demonstrate the usability of the algorithm.
2. System Overview
3. Methodology
3.1. Identification and Extraction of Artificial Landmarks
3.2. Trilateral Positioning for Mobile Construction Robot
Algorithm 1: Trilateral localization using reflectors. |
3.3. Positioning Accuracy Improvements Based on EKF
Algorithm 2: Positioning accuracy improvements based on EKF. |
Input: Estimated robot pose at an initial time: , Noise variance in the moving model: , Noise variance in the measured model: Output: Optimal pose estimation: 1 Calculate the forward state variable at time k: 2 Calculate the prediction error covariance matrix: 3 Obtain the measured current pose value from trilateral positioning: 4 Calculate the Kalman gain matrix: 5 Update the state variable at time k: 6 Calculate the estimated error covariance: 7 Return |
4. Verification Experiments
4.1. Initialization and Experimental Settings
4.2. Validation with Gazebo
4.3. Testing in Physical Environments
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Axis | 1# | 2# | 3# | 4# | 5# | 6# | 7# |
---|---|---|---|---|---|---|---|---|
True value | X-axis | −0.89 | 3.58 | 3.87 | −0.41 | −4.43 | −5.52 | −5.24 |
Y-axis | −6.28 | −3.77 | 3.68 | 5.61 | 3.99 | −0.46 | −4.59 | |
Measured values | X-axis | −0.87 | 3.86 | 3.85 | −0.38 | −4.37 | −5.48 | −5.21 |
Y-axis | −6.28 | −3.77 | 3.68 | 5.61 | 3.98 | −0.46 | −4.55 | |
Euclidean distance | \ | 0.02 | 0.02 | 0.02 | 0.03 | 0.06 | 0.04 | 0.05 |
Site | Coordinate Axis | Site1 | Site2 | Site3 | Site4 | Site5 | Site6 |
---|---|---|---|---|---|---|---|
Ideal value (m) | X-axis | 0 | 3 | 0 | 3 | 0 | 3 |
Y-axis | 0 | 0 | 1.2 | 1.2 | 2.4 | 2.4 | |
Output value (m) | X-axis | 0.018 | 3.071 | −0.075 | 3.064 | 0.077 | 2.911 |
Y-axis | 0.014 | 0.031 | 1.221 | 1.315 | 2.461 | 2.382 |
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Zeng, L.; Guo, S.; Zhu, M.; Duan, H.; Bai, J. An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot. Buildings 2024, 14, 1026. https://doi.org/10.3390/buildings14041026
Zeng L, Guo S, Zhu M, Duan H, Bai J. An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot. Buildings. 2024; 14(4):1026. https://doi.org/10.3390/buildings14041026
Chicago/Turabian StyleZeng, Lingdong, Shuai Guo, Mengmeng Zhu, Hao Duan, and Jie Bai. 2024. "An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot" Buildings 14, no. 4: 1026. https://doi.org/10.3390/buildings14041026