Evidence map›Paper›PMID 30424182›Full record

ArticleMicromachines2018

Motion Constraints and Vanishing Point Aided Land Vehicle Navigation.

Zhenbo Liu, Naser El-Sheimy, Chunyang Yu, Yongyuan Qin

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
22.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

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

4 authors at 2 institutions in 2 countries.

Zhenbo LiuSchool of Automation, Northwestern Polytechnical University, Xi'an 710129, China. zhenbo.liu2@ucalgary.ca.ORCID 0000-0002-3762-9529
Naser El-SheimyDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada. elsheimy@ucalgary.ca.
Chunyang YuDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada. chunyang.yu@ucalgary.ca.
Yongyuan QinSchool of Automation, Northwestern Polytechnical University, Xi'an 710129, China. qinyongyuan@nwpu.edu.cn.
University of Calgary · CANorthwestern Polytechnical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the typical Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) setup for ground vehicle navigation, measures should be taken to maintain the performance when there are GNSS signal outages. Usually, aiding sensors are utilized to reduce the INS drift. A full motion constraint model is developed allowing the online calibration of INS frame with respect to (w.r.t) the motion frame. To obtain better heading and lateral positioning performance, we propose to use of vanishing point (VP) observations of parallel lane markings from a single forward-looking camera to aid the INS. In the VP module, the relative attitude of the camera w.r.t the road frame is derived from the VP coordinates. The state-space model is developed with augmented vertical attitude error state. Finally, the VP module is added to a modified motion constrains module in the Extended Kalman filter (EKF) framework. Simulations and real-world experiments have shown the validity of VP-based method and improved heading and cross-track position accuracy compared with the solution without VP. The proposed method can work jointly with conventional visual odometry to aid INS for better accuracy and robustness.

Indexed as

Extended Kalman filterinertial navigation systemMEMS inertial measurement unitnonholonomic constraintsrelative attitudeStochastic Cloning Kalman filtervanishing point

Identifiers

PMID30424182
PMCPMC6187376
OpenAlexW2804588044

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.