Evidence map›Paper›PMID 32316514›Full record

ArticleSensors (Basel, Switzerland)2020

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results.

Kai Zhu, Xuan Guo, Changhui Jiang, Yujingyang Xue, Yuanjun Li, Lin Han, Yuwei Chen

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kai ZhuSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Xuan GuoSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Changhui JiangSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
Yujingyang XueSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Yuanjun LiSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Lin HanSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
Yuwei ChenDepartment of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, Masala, FI-0245 Espoo, Finland.ORCID 0000-0003-0148-3609

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

Indexed as

GNSSMIMUodometerstate constraints

Identifiers

PMID32316514
PMCPMC7219075

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.