Ground Penetrating Radar-Assisted Multimodal Robot Odometry Using Subsurface Feature Matrix
Li, Haifeng Guo, Jiajun Fan, Xuanxin Wang, Huaichao Liu, Ji Koshekov, Kairat Song, Dezhen
Li, Haifeng
Guo, Jiajun
Fan, Xuanxin
Wang, Huaichao
Liu, Ji
Koshekov, Kairat
Song, Dezhen
Supervisor
Department
Robotics
Embargo End Date
Type
Conference proceeding
Date
License
Language
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Localization of robots using subsurface features observed by ground-penetrating radar (GPR) enhances and adds robustness to common sensor modalities, as subsurface features are less affected by weather, seasons, and surface changes. We introduce an innovative multimodal odometry approach using inputs from GPR, an inertial measurement unit (IMU), and a wheel encoder. To efficiently address GPR signal noise, we introduce an advanced feature representation called the subsurface feature matrix (SFM). The SFM leverages frequency domain data and identifies peaks within radar scans. Additionally, we propose a novel feature matching method that estimates GPR displacement by aligning SFMs. The integrations from these three input sources are consolidated using a factor graph approach to achieve multimodal robot odometry. Our method has been developed and evaluated with the CMU-GPR public dataset, demonstrating improvements in accuracy and robustness with real-time performance in robotic odometry tasks.
Citation
H. Li, J. Guo, X. Fan, H. Wang, J. Liu, K. Koshekov , et al., "Ground Penetrating Radar-Assisted Multimodal Robot Odometry Using Subsurface Feature Matrix," 2025, pp. 1030-1036.
Source
Proceedings International Conference on Tools with Artificial Intelligence Ictai
Conference
2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
40 Engineering, 4007 Control Engineering, Mechatronics and Robotics, 46 Information and Computing Sciences, 4605 Data Management and Data Science, 4608 Human-Centred Computing
Subjects
Source
2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI)
Publisher
IEEE