Why Autonomous Systems Need Better Positioning Infrastructure

The $48B autonomous systems market runs on centimeter-level positioning data. The correction infrastructure that provides it was built for a different era, at a different scale, for a different customer.

Centimeter-level positioning accuracy isn't a feature. For autonomous systems, it's a hard requirement.

An autonomous lawnmower that's off by a meter mows the flower bed. A drone delivering a package to the wrong address by a meter is a failed delivery. A precision agriculture sprayer applying variable-rate inputs a meter off-target wastes inputs and damages yield. In each case, the underlying limitation is the same: GPS alone isn't accurate enough, and the correction infrastructure that closes that gap is increasingly becoming the bottleneck.

This is the market context behind onocoy's infrastructure. Understanding it matters for any professional GNSS operator evaluating whether to connect their stations to the network.

The Market That Depends on Centimeter Accuracy

The autonomous systems market is projected to reach $48 billion by 2030. The segment includes autonomous agricultural machinery, commercial drone fleets, logistics and delivery robotics, construction automation, and highly automated vehicles.

Every application in that category has a positioning requirement that standard GPS can't meet. Consumer-grade GPS delivers 1 to 5 meters of accuracy under good conditions, worse in urban or obstructed environments. Most autonomous systems need accuracy in the range of 2 to 10 centimeters. Closing that gap requires RTK (Real-Time Kinematic) correction data from ground reference stations.

The mechanism works like this: a reference station with a precisely known position observes the same GNSS signals as the rover in the field. The difference between what the station observes and what the satellites broadcast is the correction. That correction is sent to the rover in real time, eliminating most of the atmospheric and signal error. The result is centimeter-level accuracy where standard GPS would give you meters.

No correction data, no autonomous systems at scale. The infrastructure is foundational.

Why Existing Correction Networks Are Struggling to Keep Up

The dominant GNSS correction networks were built by incumbents. These are serious engineering companies, and their networks work well for the applications they were designed for: professional surveyors, construction teams, and precision agriculture operators in developed markets.

The problem is scaling that model to the volume and geography that autonomous systems require.

Legacy correction networks are built around subscription pricing. A surveying company might operate a handful of rovers and pay a regional subscription fee. That model works when you have tens or hundreds of devices. When you're deploying thousands of autonomous machines across multiple countries, subscription pricing becomes a structural problem. The cost doesn't scale linearly with usage, and for many applications, the usage pattern is highly variable anyway. A drone fleet doesn't need continuous correction access. It needs correction data during flight windows.

Coverage is the second issue. Legacy networks invested in coverage where their historical customer base was concentrated: Western Europe, North America, Japan, Australia. The fastest-growing markets for autonomous systems are often in Southeast Asia, Latin America, and the Middle East. Coverage in those regions is patchy at best, nonexistent in many areas. An agricultural technology company deploying equipment in Indonesia or Vietnam can't rely on Trimble's correction network. It often can't rely on any commercial correction network.

The third issue is hardware lock-in. Many correction services are tied to proprietary receivers or antenna systems. Clients using one vendor's correction data are often effectively locked into that vendor's hardware ecosystem. For OEM manufacturers building autonomous systems, that dependency creates cost and supply chain risk.

A Different Infrastructure Model

onocoy's correction network was built on a different set of assumptions.

The stations are deployed by independent operators, companies and individuals who run compatible GNSS reference stations and contribute their data to the network. There's no central deployment team, no rollout budget, and no proprietary hardware requirement. Any compatible station works. Coverage density grows because operators in a given region see an opportunity and deploy.

The result is a network with more than 7,500 validated stations across 40+ countries, operating today. Clients access correction data on a Pay-Per-Use basis, paying per data credit consumed rather than per seat or per month. For applications with variable usage patterns, that's a meaningfully better fit.

The traction validates the model. onocoy has reached $400k+ in annual recurring revenue from B2B clients, with 35% monthly revenue growth from October through February. The end clients are in production: agriculture, drones, logistics, surveying. They found the network, tested the data quality against their existing providers, and stayed.

That growth pattern reflects what happens when correction data is accessible in markets and at price points the legacy model doesn't serve well.

What This Means for Professional GNSS Operators

If your company operates GNSS reference stations, you're already running the physical infrastructure that the autonomous systems market needs. The question is whether that infrastructure is generating revenue proportional to its value.

Legacy correction networks typically don't pay station operators for their data. onocoy does. The $ONO token reward system compensates operators based on the quality, availability, and location of their data. Stations in underserved areas, where the network needs coverage most, earn proportionally more.

For companies with existing infrastructure in regions where autonomous system deployment is growing, connecting to onocoy is a way to monetize assets that are already running. For companies evaluating new deployments, onocoy's High Value Area program identifies specific regions where enterprise client demand is highest and provides reward multipliers to incentivize coverage there.

The underlying dynamic is simple: onocoy has clients who need correction data coverage in specific geographies. It needs station operators to provide that coverage. The economics are designed to align those two sides directly.

Key Takeaways

• Centimeter-level positioning is a hard requirement for many autonomous systems in agriculture, logistics, drone operations, and automated vehicles.

• RTK correction data from ground reference stations closes the gap between standard GPS accuracy and what autonomous systems need.

• Legacy correction networks from Trimble, Leica, and Hexagon were built for professional surveyors. They struggle to scale to the volume, geography, and pricing models the autonomous systems market requires.

• onocoy operates a hardware-agnostic, community-deployed correction network with 7,500+ stations, Pay-Per-Use pricing, and growing coverage in the markets legacy providers don't serve well.

• $400k+ ARR and 35% monthly revenue growth from B2B clients in production validate that enterprise demand for this infrastructure is real.

What This Means for Enterprise Operators

The market for RTK correction data isn't slowing down. The industries that depend on precision positioning are scaling fast, and the correction infrastructure that serves them hasn't kept pace.

That's the gap onocoy is filling. If you operate GNSS stations today, the rest of this series covers what connecting them to onocoy actually looks like: the economics, the organizational setup, the technical requirements, and how to optimize your contribution to the network.

👉 See where your stations could contribute: console.onocoy.com/explorer