Aeva’s unveiling of the Omni sensor at CES 2026 is an important incremental step for robotic perception, not a panacea. The Omni is a compact, wide-view short-range 4D LiDAR built on Aeva’s FMCW platform, offering a panoramic 360 degree horizontal by 90 degree vertical field of view and onboard velocity measurement.

On paper the specifications read well for near-field autonomy: an 85 mm diameter package, IP68/IP69K ingress protection, passive cooling, and a maximum stated range of roughly 80 meters. Those traits make Omni an obvious candidate for small unmanned ground vehicles, logistics and warehouse robots adapted for battlefield support, unmanned maritime surface vessels navigating crowded harbors, and rotorcraft or multirotor platforms that need consistent short-range surround sensing.

Why FMCW matters here. Frequency Modulated Continuous Wave LiDAR allows a sensor to provide both range and radial velocity per point and gives real benefits in cluttered or high-glare environments because FMCW is intrinsically less susceptible to certain types of interference and blooming that plague pulsed LiDAR designs. For military systems operating around reflective signage, sun glint, or multiple cooperating sensors, those qualities simplify data association and reduce false positives in velocity estimates. Aeva highlights these advantages as core to Omni’s design.

That said, the real-world utility for military autonomy comes down to systems engineering, not single-sensor specs. Omni’s roughly 80 meter maximum range is short by force protection and early-warning standards. For convoy overwatch, perimeter surveillance, or counter-UAS tasks at stand-off, Omni will need to be paired with longer-range electro-optical, radar, or long-range LiDAR sensors to provide layered detection and engagement timelines. Expect Omni to excel in the near-field detection, classification, and avoidance role while relying on other modalities for strategic early warning.

Practical integration issues to watch for

  • Mounting and field of view trade-offs: a truly panoramic optical aperture reduces blind spots but forces careful placement to avoid occlusion from chassis, antennas, or weapon mounts. Omni’s compact form factor eases this, but designers must still validate vehicle-level fields of view in vehicle integration tests.
  • Power, thermal, and processor load: Omni advertises passive cooling and an onboard X1 SoC, yet real deployments with high frame rates, dense point clouds, and onboard AI will stress compute and power budgets on small UGVs and sUAS. Plan for additional processing headroom and thermal tests.
  • Environmental hardening vs maintainability: IP68 and IP69K ratings are useful for wash-down and dust. Warzone deployments add mechanical shock, EMI, and ballistic fragment exposure scenarios that demand bespoke ruggedization and replacement strategies.

Security and spoofing remain unresolved operational risks. Academic and field research across the past several years has shown practical LiDAR spoofing and object-removal techniques that can manipulate point clouds or induce false objects through reflected pulses or externally injected light. These attack vectors are not hypothetical; researchers have demonstrated mirror-based, laser-injection, and scan-matching guided spoofing attacks that can confuse perception stacks. FMCW brings advantages against some interference modes, yet it does not render a sensor immune to clever physical-layer or algorithmic spoofing. Any military adoption of Omni must include red-team spoofing campaigns and fusion-layer defenses.

Supply chain and sustainment notes. Aeva developed Omni in partnership with LG Innotek for hardware integration and manufacturing, a relationship that should help scale production and bring more predictable unit costs. For defense buyers this kind of commercial manufacturing path is a double-edged sword. It helps availability and cost but creates a dependency that must be evaluated for security, export controls, and continuity under conflict conditions. Recent reporting and filings document Aeva’s manufacturing partnerships and strategic investments in that ecosystem.

A recommended verification and fielding roadmap for defense programs 1) Laboratory signal-level characterization: measure point-cloud fidelity, velocity accuracy, noise floor, and response to controlled sunlight, dust, and precipitation. Compare FMCW returns against a pulsed reference sensor. 2) Red-team spoofing tests: run mirror-based, laser-injection, and scan-matching adversarial scenarios to quantify the sensor and stack failure modes described in the literature. 3) Multi-sensor fusion trials: validate how Omni’s short-range 4D output fuses with long-range radar, EO/IR, and GNSS-denied navigation systems under latency and bandwidth constraints. 4) Environmental and EMI hardening: exercise in blast, shock, electromagnetic pulse analogs, and continuous high-EMI urban environments to see how robust the X1 SoC and integrated software are under stress. 5) Logistics and sustainment drills: verify repair times, spare availability, and firmware update procedures under contested communications assumptions. Consider supply chain risk and export control implications tied to manufacturing partners.

Bottom line: Aeva Omni is a useful, well-engineered product for near-field 360 degree perception that will materially improve obstacle avoidance, classification, and local situational awareness for robotic systems. It is not a substitute for layered sensing or for disciplined systems engineering that anticipates spoofing, jamming, and sustainment in austere or contested operating environments. If defense integrators treat Omni as a promising component in a multi-modal perception architecture and fund the adversarial testing and hardening it needs, it can reduce risk to soldiers and enable more capable autonomous teammates. If adopters chase vendor specs without red-team validation, they will simply replace one brittle sensor with another.