Quantum RTLS provides millimeter-accurate 3D position and pose data for robots, operators, tools, and test environments. 

For humanoid robotics teams, it can act as a field-deployable ground truth layer for teleoperation, task training, motion analysis, kinematic validation, coordinate-frame alignment, and drift correction of camera-, SLAM-, or IMU-based systems. 

Traditional mocap systems rely on cameras, IR markers, controlled lighting, clear line of sight, and careful studio-style setup. That works well in a lab, but it can become difficult in warehouses, factories, pilot lines, field environments, or cluttered test spaces. 

Quantum RTLS is designed for industrial deployment. It uses Anchors and Mobiles instead of camera arrays, can cover larger operating areas, and is easier to move, expand, or reconfigure when test spaces change. 

Quantum RTLS can provide timestamped position and pose data, including: 

• Device ID 

• Timestamp 

• Sequence number 

• 3D position 

• Velocity 

• Quaternion rotation 

• Position flags 

• Quality / derivation indicators 

• Timing fields useful for synchronization and filtering

   

This makes it easier to consume the data in robotics middleware, logging pipelines, teleoperation software, simulation tools, and sensor-fusion stacks. 

Yes. Depending on the integration architecture, position and pose can be accessed through the event stream from the Edge Compute Device (ECD) or directly from the Mobile over supported connected workflows. 

That gives robotics teams flexibility: stream position data into a centralized system, pull data into a robot stack, or log Mobile-level data for testing and analysis. 

Quantum RTLS exposes data through open APIs and real-time event streams. Robotics teams can stream position and quaternion pose data into ROS2, teleoperation controllers, data recorders, simulation environments, perception-fusion stacks, or custom applications. 

A typical integration involves aligning the Quantum RTLS coordinate frame with the robot or test-space frame, subscribing to live position events, and filtering data based on flags, timestamps, and quality indicators. 

Quantum RTLS supports position updates up to 20 Hz per device, depending on device type, configuration, tracking mode, coverage geometry, and system requirements. 

For robotics use cases, the practical update rate should be evaluated together with latency, synchronization, robot speed, expected motion profile, and the downstream control or logging loop consuming the data. 

A Starter Kit with rechargeable, wireless Anchors can be used to stand up a real measurement area quickly, often in under an hour for an initial evaluation space. 

Because the system is built around deployable Anchors, Mobiles, and an Edge Compute Device (ECD), teams can create temporary positioning areas in labs, pilot lines, warehouses, test cells, or field environments without building a permanent mocap studio. Once the evaluation is complete, the same hardware becomes the first building block of a larger system. 

Quantum RTLS includes configuration and calibration tools designed to help teams deploy, tune, and evaluate the system in real environments. 

These tools support Anchor setup, Mobile configuration, calibration workflows, range visualization, device logs, and environment checks for possible sources of interference. For robotics teams working outside clean lab conditions, these tools are often important for improving position quality and understanding the operating environment. 

Good calibration depends on collecting high-quality ranging data across the tracking volume. In practice, that means moving the Mobile through the area with clear ultrasonic line of sight to the Anchors, using an appropriate calibration path, and setting system parameters.  

In complex spaces, teams may tune channel selection, Max Range, Anchor geometry, and calibration approach to improve stability and position accuracy. 

For full-precision ultrasonic positioning, the Mobile needs to be within range and have a usable ultrasonic path to the Anchor network. Mounting, occlusion, Anchor geometry, reflections, and environmental layout can all affect performance. 

Temporary obstructions can be bridged with built-in inertial propagation and sensor fusion, and position payload flags help downstream systems understand how a position was derived. For demanding robotics use cases, teams should validate update rate, latency, pose quality, and coverage in the actual test environment.