A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
Open accessAbstract
Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.
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For sensor data with and without curbs on the roadsides, the unscented Kalman filter (UKF) applies to urban areas by improving performance of previous methods for UKF.
“Sensor data with and without curbs on the roadsides [126] | Unscented Kalman filter (UKF) | Apply to urban areas by improving performance of previous methods for UKF”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [126]
The Introduction (page unavailable) proposes improving a traditional UKF-based localization method for autonomous vehicles in urban environments, with additional lateral correction from detected curbs. The Experimental Results (page unavailable) compare UKF with and without GNSS-jump detection—not sensor data from roads with and without roadside curbs. That curb-condition comparison is unsupported. (LLM, conf=0.87)
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Flagged first, then by citations
Research works printed but not linked: 1
- [2]Ronnback S. Development of an INS/GPS Navigation Loop for an UAV Master’s Thesis Lulea Tekniska Universitet Lulea, Sweden 2000
Cited by
Sorted by citations
Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 41 · see citing works
- Authors
- Meng, X., Wang, H., & Liu, B.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Northeastern University (CN)
- Corresponding author
- Wang, Heng
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-09-18
- Volume
- 17
- Issue
- 9
- Pages
- 2140
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5621051
- OpenAlex
- W2756242387
- Cited by
- 124
- References
- 30
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 21 · 2023: 20 · 2022: 20 · 2021: 18 · 2020: 14 · 2019: 6 · 2018: 7
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- vehicle localization, autonomous vehicles, distance measuring instrument, LiDAR, fault detection
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