Quadruped Robot SLAM Patents: Leaders, Trends & White Space 2026
- Filing already peaked. 2018 recorded 11 filings against a 2022 midpoint of 6 — this is a field cooling rather than accelerating, even before the 2026 partial-year figure is adjusted upward for publication lag.
- Control claims dominate, perception claims lag. G05D (control of non-electric variables) covers 45 of 59 records versus 5 for G06V (image/video recognition) — vision-based terrain understanding is comparatively thin ground.
- Ownership is fragmented, not consolidated. Only four co-assignee pairs exist across 59 families, and recent-year momentum shows most tracked assignees at zero new filings in the latest year — the field has no single filer pulling ahead.
Filing growth compares 2021 (8 records) with 2024 (8) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 59 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This landscape tracks patent families at the intersection of legged locomotion and autonomous navigation: quadrupedal and legged robot platforms combined with SLAM, terrain-aware navigation and localization-and-mapping claims. The search spans control (G05D1/02), navigation instruments (G01C21/16) and leg-based drive mechanisms (B62D57/032), which together define the boundary between a robot that walks and a robot that knows where it is walking.
Fifty-nine families is a small corpus for a technology category this specific, and the receiving-office spread — concentrated in China, India and the United States, with a modest EPO and PCT presence — points to a field still filing regionally rather than building global patent families. Publication lag of roughly 18 months means the 2025 and 2026 figures will rise as later filings surface; treat the most recent two years as a floor, not a ceiling.
Trend and technology composition
The filing curve and the IPC breakdown together show a field that built its control and mechanical foundations early and has not yet produced a comparable wave of perception-layer claims.
A 2018 peak, then a plateau
Filings rose from 3 in 2017 to a peak of 11 in 2018, then settled to a midpoint of 6 by 2022 — flat-to-declining rather than growing. The 2026 figure of 5 is a partial year and will understate the true total once later publications land.
Control and drivetrain classes carry the volume
G05D (control of non-electric variables) and B62D (motor vehicles & steering) account for 45 and 29 records respectively, out of 59 total — locomotion control and leg mechanics are the most heavily claimed territory. G06V (image/video recognition) and G06N (AI-based computing) sit at 5 and 3, marking the perception and learning layers as comparatively open.
Shares are the percentage of the 59 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Quadruped Robot SLAM and Navigation with Eureka
This page is one run against one query. Ask Eureka your own question about quadruped robot slam and navigation and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited prior art and a representative filing
Motion control for a legged robot with independent foot-end regions
The present disclosure discloses a motion control method and apparatus for a legged robot, a legged robot, a computer-readable storage medium, and a computer program product. The legged robot includes at least two foot-ends. The method includes receiving a bound instruction for the legged robot when its foot-ends stand in unit regions independent from one another, and controlling the legged robot to bound to a target unit region in response to that instruction.Filed by Tencent Technology (Shenzhen) Company Limited, published 2024-07-17 as EP4400930A1.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | CN107085422A | 一种基于Xtion设备的多功能六足机器人的远程控制系统 | 41 |
| 2 | CN111752285A | 四足机器人自主导航方法、装置、计算机设备及存储介质 | 34 |
| 3 | CN119469168A | 面向特种环境的四足机器人自主导航方法及系统 | 27 |
| 4 | US20200333790A1 | Control device, and control method, program, and mobile body | 20 |
| 5 | US20200387162A1 | Control device and control method, program, and mobile object | 15 |
| 6 | CN116222543A | 用于机器人环境感知的多传感器融合地图构建方法及系统 | 14 |
| 7 | CN115793649A | 一种电缆沟自动巡检装置及巡检方法 | 14 |
| 8 | WO2019131198A1 | Control device, control method, program, and mobile body | 14 |
| 9 | CN113885510A | 一种四足机器人避障及领航员跟随方法及系统 | 9 |
| 10 | WO2019111701A1 | Control device, control method, program, and moving body | 9 |
Citation counts reflect influence within this searched corpus and skew toward older filings; they are not a measure of current commercial relevance.
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 patterns matter more than the raw count: where claim density already sits, where citation weight concentrates, and how thin the co-filing network is.
Control claims are the crowded lane
Locomotion and gait control account for the bulk of filings. A new control-layer claim needs to differentiate sharply from existing bound-instruction and foot-region approaches already on file.
Older Chinese filings anchor the prior art
The highest-cited records are earlier six-legged and four-legged autonomous-navigation filings from Chinese applicants. High citation counts here reflect age and searchability, not that these remain the state of the art.
Almost no joint filing
With only four co-assignee pairs identified, most families are filed by a single entity. That limits the value of co-filing analysis for spotting alliances and suggests IP strategy here is largely independent, not collaborative.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to quadruped robot slam and navigation, with the prior art for and against each one.
Assignees and where the field is still open
No single assignee shows sustained recent-year momentum: the tracked entities each show zero-to-one filings in the latest year, consistent with a fragmented field rather than a consolidating one.
Recent activity is thin across the board
Among the assignees tracked for recent-year momentum, only Shenzhen University shows a filing in the latest year; Sony Group, Tencent, Qilu University of Technology, SUNENG (Shanghai) Automation and Yale University all show zero. This is consistent with the flat-to-declining trend at the aggregate level.
China leads receiving-office volume, but not by a wide margin
China accounts for the largest single share of receiving-office filings, with India and the United States following. The spread across six receiving offices, including WIPO/PCT and EPO, indicates the technology is being protected regionally rather than through a single dominant global filing strategy.
Cross-institution filing is rare
The strongest co-assignee links pair a university with an industrial robotics partner, or a research institute with a state utility — one-off collaborations rather than a repeated pattern. Most families in this corpus carry a single named assignee.
| Assignee | Recent year | YoY |
|---|---|---|
| Shenzhen University | 1 | — |
| Sony Group Corporation | 0 | — |
| Tencent Technology (Shenzhen) Company Limited | 0 | — |
| Qilu University of Technology (Shandong Academy of Sciences) | 0 | — |
| SUNENG (Shanghai) Automation Technology Co., Ltd. | 0 | — |
| Yale University | 0 | — |
| Jiangsu Electric Power Test & Research Institute Co., Ltd. | 0 | — |
| Wuhan Huazhong Sineng Technology Co., Ltd. | 0 | — |
Where to take this analysis
The aggregate numbers point to specific questions worth running against the full family set before committing to a filing or freedom-to-operate position.
Map claim scope against the G05D cluster
Pull the independent claims from the highest-density control-class families to see exactly how bound-instruction and gait-control claims are worded, so a new filing can be drafted around rather than into them.
Explore claim scope in EurekaTest the white-space branches
Vision-based terrain classification and multi-leg sensor fusion show thin IPC coverage; run a targeted search on those specific claim elements before assuming the space is genuinely open.
Run a white-space search in EurekaTrack the EP4400930A1 family
As a recent, actively prosecuted Tencent filing, this family's prosecution history and any continuations are worth monitoring for scope changes that affect freedom-to-operate.
Track this family in EurekaCommon questions on this landscape
No single assignee dominates this corpus of 59 families; filings are spread across Chinese universities, robotics companies and a handful of larger technology firms including Sony Group and Tencent. Recent-year momentum data shows most tracked assignees at zero new filings in the latest year, with Shenzhen University the only one showing recent activity. This fragmentation means competitive tracking should focus on the highest-cited individual records rather than assuming a market leader exists.
The filing trend peaked at 11 records in 2018 and had fallen to a midpoint of 6 by 2022, which is flat-to-declining rather than growing. The 2026 figure of 5 is a partial year and should rise once later filings publish, since publication typically lags filing by around 18 months. Overall the pattern looks like an early wave of foundational filings followed by a plateau, not a technology still in its growth phase.
The core classes are G05D1/02 (control of non-electric variables, used for locomotion and gait control), G01C21/16 (navigation instruments and dead-reckoning), and B62D57/032 (vehicles with leg-based drive). Across the 59-family corpus, G05D accounts for 45 records and B62D for 29, making control and mechanical locomotion the most heavily claimed areas. Perception classes like G06V (image recognition, 5 records) and G06N (AI computing, 3 records) are comparatively underrepresented.
The clearest gaps sit in vision-based terrain classification and multi-leg sensor fusion for SLAM, both of which fall under IPC classes with low record counts (G06V at 5, G06N at 3) relative to the 45 records in control-layer G05D. Learning-based gait adaptation and radar/sonar terrain mapping fusion also show thin coverage. These are areas where claim space is not yet occupied, though that should be confirmed with a targeted search before filing.
EP4400930A1, filed by Tencent Technology (Shenzhen), covers a motion control method for a legged robot with independent foot-end regions, specifically a bound instruction that moves the robot between unit regions where its feet stand. It is a control-layer claim, which sits in the most heavily filed IPC class in this corpus. Anyone drafting gait-control or foot-placement claims should review its specific claim language on foot-end unit regions and bound instructions, since that mechanism is precisely defined rather than broadly framed.
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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.