Evidence map›Paper›PMID 32781582›Full record

ArticleSensors (Basel, Switzerland)2020

VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization.

Jingzhe Wang, Leilei Li, Huan Yu, Xunya Gui, Zucheng Li

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jingzhe WangSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0002-1902-9271
Leilei LiSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.
Huan YuSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-9434-3693
Xunya GuiSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.
Zucheng LiSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.

Funding

Equipment Pre-development Project 41417070401National Natural Science Foundation of China 91420203; 90920304; 91120010
6 · The paper itself

Abstract

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system's viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

Indexed as

magnetic informationnon-linear optimizationvisual-inertial navigationyaw estimation

Identifiers

PMID32781582
PMCPMC7472289

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.