Label Every Sensor, Perfectly in Sync
Synchronize and annotate RGB, depth, LiDAR, IMU, and force/torque streams in one connected workflow — the fused, time-aligned ground truth physical AI depends on.
Physical AI robots do not perceive the world through a single camera. They fuse synchronized streams — RGB cameras, depth sensors, LiDAR, IMU, and force or torque readings — into one coherent picture of the environment. Annotating that multimodal data is fundamentally different from labeling a single video: every modality has to stay frame-accurate and time-aligned, or the fused training signal breaks down. Annotera specializes in exactly this.
Our annotators work with synchronized multi-sensor data in a single, connected workflow, producing consistent labels across modalities — 3D bounding boxes and segmentation on point clouds, object correspondence between camera and LiDAR, and event alignment across IMU and force/torque traces. With 20+ years of outsourcing expertise and 350+ trained specialists, Annotera delivers sensor-fusion annotation at the scale autonomy, manipulation, and humanoid programs require.
As robots add more sensors and the annotation standards for physical AI are still taking shape, the teams with clean, fused, time-aligned ground truth will train the most reliable perception. Annotera helps you build it.
Physical AI robots do not perceive the world through a single camera. They fuse synchronized streams — RGB cameras, depth sensors, LiDAR, IMU, and force or torque readings — into one coherent picture of the environment. Annotating that multimodal data is fundamentally different from labeling a single video
Objects in point clouds are labeled with 3D boxes and segmentation. As a result, robots gain accurate spatial perception of their surroundings.
Objects are matched between camera images and LiDAR point clouds. Therefore, fused perception models learn consistent cross-modal identity.
RGB-D streams are annotated with object and surface labels. In addition, this supports grasp planning and close-range manipulation.
Motion events are aligned to IMU traces and the visual stream. Consequently, models learn to associate movement with sensed dynamics.
Contact and force events are labeled and time-aligned with video and motion. Moreover, this gives manipulation models a tactile-adjacent signal.
All modalities are validated for frame-accurate temporal alignment. As a result, the fused dataset stays coherent across sensors.
Frame-Accurate Synchronization
Rigorous time-alignment across every modality keeps fused labels coherent — the non-negotiable requirement for sensor-fusion training.
Full Multimodal Coverage
RGB, depth, LiDAR, IMU, and force/torque handled in one connected workflow, rather than stitched together from separate vendors.
Scalable, Secure Delivery
SOC-compliant workflows and flexible capacity scale multi-sensor annotation to production volume without compromising accuracy or security.
Physical AI robots do not perceive the world through a single camera. They fuse synchronized streams — RGB cameras, depth sensors, LiDAR, IMU, and force or torque readings — into one coherent picture of the environment. Annotating that multimodal data is fundamentally different from labeling a single video
Proven Expertise
20+ years of BPO experience applied to complex multimodal robotics data.
Single Connected Workflow
All sensors labeled together, preserving cross-modal correspondence and timing.
3D & Point-Cloud Depth
Established 3D cuboid and segmentation capability extended to fused sensor data.
Flexible Scaling
Capacity scales from pilot rigs to full autonomy datasets.
Consistent Quality
Multi-layer validation keeps labels accurate across every modality.
Secure Workflows
SOC-compliant handling with strict access controls and US onshore options.
Here are answers to common questions about text annotation, accuracy, and outsourcing to help businesses scale their NLP projects effectively.
What is multi-sensor fusion annotation?
It is the synchronized labeling of multiple sensor streams — RGB, depth, LiDAR, IMU, and force/torque — in one connected workflow, with every modality kept frame-accurate and time-aligned. As a result, robots learn from a coherent, fused view of the world.
Why do robotics teams need sensor-fusion annotation?
Physical AI robots fuse many sensors to perceive and act, and the fused training signal only works if labels are consistent and time-aligned across modalities. Therefore, specialized multi-sensor annotation is essential for reliable perception.
Which sensors can Annotera label?
We annotate RGB cameras, depth sensors, LiDAR point clouds, IMU motion data, and force/torque readings, including 3D boxes, segmentation, cross-sensor correspondence, and event alignment. Moreover, the label set is tailored to each sensor configuration.
How is this different from standard video or image annotation?
Single-stream annotation labels one modality at a time. Sensor-fusion annotation, however, must preserve correspondence and timing across several modalities at once, which requires 3D and point-cloud expertise plus rigorous synchronization.
Can Annotera scale multi-sensor annotation?
Yes. With 350+ trained specialists and SOC-compliant, flexible delivery, we label large multimodal datasets while keeping every modality accurate, aligned, and secure.
