Extreme-range detection starts with the marker.
RangeMark combines passive visual markers, long-range marker detection, and deterministic identification workflows to help drone and remote systems detect markers beyond conventional visual-code range limits, then decode and confirm identity when image conditions allow.
THE DISTANCE CHALLENGE
Long-range identification starts with detection.
Most machine-readable systems were designed for close-range use. They work when the camera can get near the marker and capture enough detail to decode it reliably.
Drone operations create a different requirement. Markers may need to be detected and identified from hundreds of meters away, where distance, resolution, marker size, optics, and conditions all affect performance.
RangeMark was built for that challenge.
PASSIVE VISUAL MARKERS
RangeMark markers can be printed, placed, or integrated into the environment. They require no power source, transmit no signal, and emit no RF.
Each marker carries a unique machine-readable identity. The system can detect the marker at long range, then decode and resolve its identity when sufficient image detail is available.
LONG-RANGE DETECTION
RangeMark’s detection performance is enabled by the topology of the code: the spatial structure, contrast, and pattern geometry that allow the system to locate the marker in the scene at distance..
Detection and decoding range depend on marker size, camera resolution, optical zoom, viewing angle, motion, lighting, and operating conditions.
MARKER FORMATS
Different formats for different needs.
RangeMark is built on Sodyo’s patented visual-code platform, with marker formats designed for different operational requirements.
Marker format, size, placement, optics, distance, angle, motion, and environmental conditions all influence detection and identification performance.
Additional spectrum formats are under development.
COLOR CODE
A colored marker format designed for visible-light conditions, supporting high-density marker encoding and long-range detection performance.
CARBON CODE
A duo-tone marker format designed for challenging visibility conditions, where geometric decoding can support detection across broader sensing environments.
In tested configurations, marker detection at long distance can occur within a camera field of view covering approximately 20 hectares / 49 acres.
RANGE MODEL
Range scales with marker size and optics.
RangeMark is not limited to a single fixed detection distance.
Detection performance is influenced by marker size, camera resolution, optical zoom, viewing angle, motion, lighting, and operating conditions. Larger markers and suitable optical hardware can extend the distance at which a marker can be detected, decoded, and identified.
The practical takeaway: as marker size and optical setup scale, the usable detection distance can scale with them.
TWO-STAGE WORKFLOW
Detection first. Identification second.
RangeMark separates finding the marker from confirming its identity.
Detection locates the marker in the visual field. Identification decodes the marker and resolves it to a unique machine-readable identity.
The result is a confirmed identification from distance, not a visual guess.
DETERMINISTIC VERIFICATION
Not an estimate. A confirmed identity.
AI vision can estimate what something appears to be. RangeMark decodes an encoded marker to determine which specific marked object, location, or asset is present.
Once decoded, the marker returns a specific machine-readable identifier, providing a deterministic result rather than a visual guess.
OFFLINE-CAPABLE IDENTIFICATION
Detection and decoding without continuous connectivity.
RangeMark can detect and decode the marker locally, without depending on continuous network access.
Connectivity may support external records, updates, audit logs, or broader operational workflows, but the core detection, decoding, and identity-matching process can happen offline.
DRONE-STACK COMPATIBILITY
Built to complement existing drone systems.
RangeMark adds long-range marker detection and deterministic identification without replacing the systems already responsible for navigation, perception, mapping, control, and autonomy.
It works alongside GPS, VIO, SLAM, LiDAR, AI vision, and other drone technologies, adding a deterministic layer for detecting and identifying marked objects and locations from distance.
Integration may take place through drone-side compute, payloads, controllers, docks, infrastructure, or partner software, depending on the platform and workflow.
