IN Brief:
- FieldAI has bought MOVIA sensors for further evaluation in government and industrial autonomous systems.
- The collaboration covers IRIS ground-vehicle testing and a MOVIA Air Plus aerial mapping demonstration integrated with ATAK.
- The manufacturers have not identified a production supply contract or a wider deployment schedule.
MicroVision and FieldAI are extending their work on lidar perception for autonomous ground vehicles and uncrewed aircraft. FieldAI has purchased MicroVision MOVIA sensors for development and evaluation across its government and industrial programmes, following trials of IRIS long-range lidar on ground vehicles and airborne mapping demonstrations using MOVIA Air Plus. The purchases establish further engineering activity, with integration decisions to be made across the different robot and mission configurations.
FieldAI’s federal team previously assessed IRIS on autonomous ground vehicles, while the airborne demonstration mounted MOVIA Air Plus on a Robinson Unmanned SPIRIT platform. The system generated real-time, colourised three-dimensional mapping and integrated the output with the Android Team Awareness Kit, or ATAK. This combines vehicle sensing, map construction, and the presentation of geospatial information to field teams. The expanded work brings those technologies into further evaluations across FieldAI’s ground and aerial platforms.
Lidar determines distance by measuring the return of emitted light from surfaces in the sensor’s field of view. Combining many returns forms a point cloud representing structures, obstacles, and terrain, which perception software can use for localisation and route planning. The usefulness of the resulting model depends on the density and consistency of measurements, the sensor’s mounting position, and how observations are aligned as the vehicle moves. Cameras can add information about visible surfaces where the geometry alone does not establish the object type.
Ground robots and aircraft place different demands on perception hardware. A moving ground vehicle can use long-range measurements to identify obstacles early enough to change its path, alongside closer sensing for the area around the machine. An aircraft collecting a survey map must track its own position and combine measurements taken from changing viewing angles. FieldAI is examining the IRIS and MOVIA families against these different requirements before choosing how sensors will feed the autonomy software on each platform.
MicroVision lists a measurement range of 0.15–80m for MOVIA Air Plus, with an accuracy specification of 3cm and a 120° horizontal by 75° vertical field of view. The Air Plus unit combines lidar and camera information with edge processing and simultaneous localisation and mapping, or SLAM. Its specified range matches that of the lighter MOVIA Air, but the two products have different fields of view: the Air model is listed at 60° by 37.5°. The wider Air Plus geometry changes the area sampled during each aircraft pass.
Building an accurate live map requires more than recording a sequence of distance measurements. SLAM estimates how the sensing platform moves while joining observations into a common coordinate system, and camera input can assist the interpretation of visible features. Sensor fusion and onboard processing can make that map available during a flight instead of only after the aircraft returns. Changes in vibration, feature density, and the reliability of movement estimates can still affect the quality of the resulting spatial data.
The ATAK integration demonstrated an additional route from onboard mapping to operational use. A three-dimensional representation produced during flight can give teams a current view of terrain or structures while an inspection or reconnaissance activity continues. Delivering useful information through that chain requires the aircraft, lidar, compute platform, map software, and operator interface to exchange and time-align data reliably. MicroVision and FieldAI have demonstrated that combination, with further programme-specific evaluation still under way.
Industrial robots working in yards, plants, or other unstructured environments encounter reflections, dust, vegetation, changing illumination, and moving obstacles. These conditions affect how reliably perception software separates hazards from background returns. Engineers must also manage the sensor’s mass, electrical demand, data interface, and processing load within the vehicle’s system budget. FieldAI’s programme can test those interactions across different platforms, including circumstances in which a useful mapping output does not automatically produce a sufficiently fast vehicle-control response.
MicroVision’s earlier work with Robinson Unmanned established an aerial integration route, while the extension to FieldAI creates additional ground and airborne evaluations. The purchased MOVIA sensors will support ongoing development across federal and industrial applications. Neither company has specified a production volume, completed qualification programme, or deployment date for the expanded collaboration. Those decisions depend on how the combined sensing and autonomy arrangements perform in the intended robotic systems.
