Tightly Coupled GNSS INS Patents: Who Leads, Where the Gaps Are 2026
- Still accelerating. filings rose from 0 in 2017 to a peak of 14 in 2025, with the 2026 count already at 4 despite the year being incomplete.
- No single dominant filer. the leader holds only 3 of 39 records, and the top 5 combined account for just 28.2% of all records in scope.
- One class dominates the claim space. G01C (navigation and gyroscopes) appears in 84.6% of records, while railway-specific control (B61L) shows up in only one.
What the filing record shows
Tightly coupled GNSS/INS integration sits at the intersection of navigation, sensor fusion and optimisation. This landscape covers 39 published records filed between 2015 and mid-2026, drawn from claims and abstracts referencing factor graph optimisation, position drift correction, integrity monitoring and fault exclusion. The corpus is small relative to adjacent fields such as broad autonomous-driving perception, which is itself informative: it means the claim space is not yet locked down by a handful of dominant portfolios.
Filing activity is concentrated in China, which accounts for the large majority of receiving-office activity, with a small number of filings routed through Hong Kong, India and the WIPO PCT system. The technology composition leans heavily on core navigation and positioning classes, with a meaningful but secondary presence in image processing and general-purpose computing classes — consistent with fusion architectures that increasingly pull in visual or LiDAR data alongside GNSS and inertial measurements.
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Filing trend and technology composition
Two views of the same 39-record corpus: how filing volume has moved year over year, and which IPC subclasses the claims actually sit in.
Filings are accelerating, not plateauing
Filings moved from 0 in 2017 through a midpoint of 3 in 2022 to a peak of 14 in 2025. Because publication typically lags filing by around 18 months, the 2026 figure of 4 understates true filing activity for that year rather than signalling a slowdown.
Navigation and positioning classes dominate
G01C (distance, navigation and gyroscopes) covers 84.6% of the 39 records and G01S (radar, sonar and positioning) covers 53.8% — expected for a GNSS/INS corpus. The presence of G06T (23.1%) and G06F (15.4%) points to fusion architectures pulling in image or general compute pipelines alongside the core navigation stack; B61L (railway traffic control) appears in only one record, suggesting rail-specific integration claims remain sparse.
Shares are the percentage of the 39 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Tightly Coupled GNSS INS Integration with Eureka
This page is one run against one query. Ask Eureka your own question about tightly coupled gnss ins integration and every answer comes back with the patent numbers behind it.
Try EurekaRepresentative and most-cited filings
WO2026091186A1 — High-dimensional sequential graph optimisation with null-space dimensionality reduction for covariance estimation
The filing, from Tersus GNSS, describes a covariance estimation method built on sequential factor graph optimisation over sliding-window sensor measurements. It linearises a high-dimensional parameter space, computes the null space of the parameters to be estimated, and maps the covariance solution into a reduced-dimension space — aiming to preserve estimation optimality while sharply cutting the computation time needed to solve for covariance.Filed 2026-05-07 via the WIPO PCT route.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | CN116817928A | 基于因子图优化的卫导/惯导列车多源融合定位的方法 | 13 |
| 2 | CN119780955A | 一种基于激光与视觉融合的自主导航方法及相关设备 | 8 |
| 3 | CN120403599A | 超宽带激光雷达惯导协同SLAM方法和系统 | 7 |
| 4 | CN115326068A | 激光雷达-惯性测量单元融合里程计设计方法及系统 | 6 |
| 5 | CN118293900A | 一种基于IEKF的快速三维激光惯导耦合SLAM方法及系统 | 5 |
| 6 | CN115326101A | 实时高精度鲁棒的紧耦合视觉惯性里程计方法及系统 | 5 |
| 7 | CN116989763A | 一种面向水陆两用无人系统的融合定位与建图方法 | 4 |
| 8 | CN120085308A | 基于四锚点UWB位姿动态质量评估的无人机融合定位方法 | 3 |
| 9 | CN116804557A | 一种融合UWB测距信息的单目视觉车辆定位系统及其方法 | 3 |
| 10 | CN115711616A | 一种室内室外穿越无人机的平顺定位方法及装置 | 3 |
Citation counts favour older records that have had more time to accumulate citations within the searched corpus; treat them as a signal of influence, not of current technical importance.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Each row carries its publication number; clicking a row searches Eureka by that number.
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Three read-throughs from the trend, class and citation data that matter more than the raw counts on their own.
The curve is still climbing
Filings went from 0 in 2017 to 14 in 2025, with 2022's midpoint of 3 showing the acceleration only really started in the back half of the window. A 2026 count of 4 is not a slowdown — publication lag of roughly 18 months means recent-year filings are always undercounted at the point of any given data cut.
No single portfolio controls the space
The leading assignee holds only 3 of the 39 records in scope, and the top 5 combined reach just 28.2% of all records. That is a long tail rather than a gatekeeper structure — new entrants are not filing into a space already fenced off by one or two dominant players.
Vision-assisted fusion is a secondary but real thread
Core navigation classes G01C and G01S cover the large majority of records, as expected. But G06T (image data processing) appears in 23.1% of records and G06V (image/video recognition) in 5.1%, indicating a meaningful minority of filings fuse GNSS/INS with visual or LiDAR-derived data rather than treating inertial fusion as a standalone problem.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to tightly coupled gnss ins integration, with the prior art for and against each one.
Who is filing, and where the field is open
The ranked list covers 32 companies across the 39 records in scope — not a top-50 or top-100 cut, but the entire ranking the dataset returns. Momentum by assignee is uneven: several of the more active historical filers show no activity in the latest year.
A narrow lead, not a dominant position
The top-ranked assignee holds 3 of the 39 records in scope, only one filing ahead of fifth place at 2. That gap is thin enough that a well-timed filing programme could shift the ranking within a year or two of activity.
Several historical filers have gone quiet
Assignees that were active earlier in the window show zero filings in the latest year and a -100% year-over-year change, while at least one university shows a single filing in the latest year. This is consistent with a field where academic and applied filers rotate in and out rather than sustaining continuous filing programmes.
Joint filings are rare
The corpus shows only one identifiable co-assignee pairing, between a technical university and a coal-mining company — an applied use case rather than a broad research consortium. Most records in scope are filed by a single assignee, which limits visibility into which organisations are co-developing fusion architectures.
| Assignee | Recent year | YoY |
|---|---|---|
| Southeast University | 1 | — |
| Tongji University | 0 | -100% |
| The Hong Kong Polytechnic University | 0 | -100% |
| Suzhou Tiensor Navigation Technology Co., Ltd. | 0 | — |
| Tata Consultancy Services Ltd. | 0 | — |
| Nanjing University of Aeronautics and Astronautics | 0 | — |
| Beijing Jiaotong University | 0 | -100% |
| Chongqing University | 0 | -100% |
Where to take this next
The dataset points to a field that is still forming rather than settled — useful for scoping a filing strategy or a freedom-to-operate check before committing engineering time.
Map claims against your own architecture
Run your specific fusion approach — filter-based, factor-graph, or hybrid — against the most-cited records to see which claim language is already occupied.
Explore in Patsnap EurekaTrack the accelerating filers
With filings still climbing into 2025-2026, set up ongoing monitoring rather than treating this as a one-time landscape snapshot.
Set up monitoring in Patsnap EurekaAssess the under-claimed branches
Railway-specific integrity monitoring and vision-assisted initialization both show thin filing density relative to core navigation classes — worth a closer look before assuming the space is closed.
Dig into white space in Patsnap EurekaCommon questions about this landscape
Tightly coupled integration fuses raw GNSS measurements (pseudoranges, carrier phase) directly with inertial data in a single estimator, rather than combining separate position solutions as in loosely coupled designs. This lets the system keep working when too few satellites are visible for a standalone GNSS fix, which is why it shows up heavily in claims around position drift and initialization. For filing strategy, it matters because tightly coupled claims tend to sit in the dense G01C/G01S overlap that covers the large majority of this corpus, so novelty usually has to come from the fusion method rather than the coupling architecture itself.
The ranking covers 32 assignees across the 39 records in scope, and it is a long tail rather than a concentrated field: the top-ranked assignee holds only 3 records, and the top 5 combined reach 28.2% of all records. Filers include Chinese universities, navigation-focused companies, and at least one enterprise IT vendor, with the receiving-office data showing China as the dominant jurisdiction. No single organisation currently holds a blocking position across the whole technology.
It is growing, and the growth is still accelerating rather than levelling off. Filings moved from 0 in 2017 through 3 at the 2022 midpoint to a peak of 14 in 2025. The 2026 figure of 4 looks lower only because publication lags filing by roughly 18 months, so recent-year counts are always undercounted at any given data cut-off.
WO2026091186A1 is a PCT filing from Tersus GNSS describing a covariance estimation method that uses sequential factor graph optimisation with null-space dimensionality reduction to cut the computation time needed for real-time covariance solving. It is useful as a representative filing because it sits squarely in the factor-graph optimisation formulation that much of this corpus revolves around, and its PCT route signals an intent to pursue protection beyond a single jurisdiction, unlike most of the China-only filings in this dataset.
The technology composition data points to a few thinly filed branches relative to the core navigation classes: railway-specific integration (B61L appears in only one of 39 records), wireless-assisted correction methods (H04W at 5.1%), and vision-recognition-assisted fusion (G06V at 5.1%). These low shares do not mean the underlying problems are unsolved, but claim density there is much lower than in the core G01C/G01S navigation classes, which is where a first-mover filing has more room to establish a distinct claim scope.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.