Robots do not understand the physical world through a single sensor. Autonomous and embodied AI systems combine RGB cameras, depth sensors, LiDAR, IMU data, and force/torque signals to understand objects, movement, distance, contact, and environmental context. Training these systems requires more than labeling each sensor independently—it requires ground truth that remains consistent across every modality.
Annotera provides multi-sensor fusion annotation services that connect visual, geometric, inertial, and contact data within a unified annotation workflow. Our teams create 2D and 3D object labels, point-cloud segmentation, camera-LiDAR correspondence, depth-aware annotations, motion events, contact events, and cross-sensor temporal relationships.
The critical requirement is alignment. A camera frame, LiDAR scan, IMU event, and force reading may represent the same physical event at different timestamps or in different coordinate systems. Our annotation process preserves these relationships so robotics models learn the correct spatial and temporal relationships rather than conflicting signals.
With 20+ years of outsourcing expertise and 1,500+ trained annotators, Annotera supports robotics programs from pilot datasets to production-scale annotation workflows.
Annotate objects and environments directly within 3D point clouds using precise cuboids, semantic segmentation, and object attributes. Labels can capture object position, dimensions, orientation, ground surfaces, obstacles, and navigable regions. Our LiDAR annotation workflows provide the geometric ground truth needed for 3D perception, localization, obstacle detection, navigation, and manipulation. Where required, LiDAR labels are validated against corresponding camera or depth observations to maintain cross-modal consistency.
Match the same physical objects across camera images and LiDAR point clouds to create consistent cross-modal object identities. Annotators establish correspondence between 2D visual evidence and 3D geometric representation, accounting for visibility, object boundaries, spatial position, and sensor perspective. This enables perception models to learn how information from different sensors describes the same object.
Annotate RGB-D streams with object labels, semantic classes, surfaces, depth-aware spatial information, and manipulation-relevant attributes.
Combining appearance with depth enables robotics models to understand not only what an object or surface is, but where it is in 3D space. This is particularly valuable for grasp planning, object interaction, close-range navigation, collision avoidance, and robotic manipulation.
Annotate acceleration, angular velocity, orientation, and motion events while maintaining their temporal relationship with visual and spatial sensor streams. Our workflows identify events such as movement onset, rotation, acceleration, deceleration, stops, and other program-specific motion states, then align them with the corresponding camera and LiDAR observations.
Label force and torque measurements alongside the visual, depth, and motion data captured during robot interaction. Annotations can identify contact onset, contact duration, force changes, torque events, grasp interactions, pushing, pulling, slip, and task-specific contact states. This provides manipulation models with information that cannot be obtained from vision alone and helps connect observed actions with their physical consequences.
Validate that events observed across multiple sensors correspond to the same point in time.
Our synchronization workflow checks timestamp offsets, dropped frames, sensor drift, interpolation issues, and event alignment before data moves into downstream training workflows. Temporal synchronization is treated as a quality-control requirement rather than an afterthought, helping prevent false correlations from entering multimodal training datasets.
We align annotations across camera, LiDAR, depth, IMU, and force/torque streams while validating timestamps, event boundaries, and sensor offsets.
Handle RGB, depth, LiDAR, IMU, and force/torque annotation within one connected workflow rather than combining independently labeled datasets after the fact.
Scale annotation capacity from pilot programs to production datasets through managed workflows, secure access controls, and structured quality processes.
Robotics datasets require more than high-volume labeling. They require annotators who understand how different sensors represent the same physical environment and how annotation errors can affect downstream perception and manipulation models. Annotera combines domain-trained teams, multimodal workflows, 3D annotation expertise, structured QA, and scalable delivery to help robotics organizations convert complex sensor logs into consistent training data.
20+ years of outsourcing experience combined with dedicated teams trained for robotics and multimodal annotation workflows.
RGB, depth, LiDAR, IMU, and force/torque data can be annotated within one coordinated process, preserving cross-sensor relationships.
Experienced teams support 3D cuboids, point-cloud segmentation, object correspondence, and geometry-focused annotation requirements.
Scale from pilot datasets to recurring production volumes while maintaining defined taxonomies and QA requirements.
Annotation protocols can incorporate motion, contact, manipulation, object interaction, and task-specific physical events.
Structured access controls, secure workflows, and compliance-oriented delivery options support sensitive robotics and AI datasets.
Scale from pilot datasets to recurring production volumes while maintaining defined taxonomies and QA requirements.
Here are answers to common questions about Multi-Sensor Fusion Annotation and why accurately aligned sensor data is essential for enabling robots and autonomous systems to perceive, understand, and navigate complex real-world environments.
Multi-sensor fusion annotation is the synchronized labeling of data from multiple sensor modalities — RGB cameras, depth sensors, LiDAR point clouds, IMU motion data, and force/torque readings — in a single connected workflow that preserves frame-accurate timing and cross-modal correspondence throughout. It is distinct from annotating each sensor stream separately and merging the results: the labels must be established simultaneously across modalities, because the cross-modal correspondence that makes fused perception work cannot be reconstructed after the fact from independently labeled streams.
Physical AI robots perceive and act through multiple sensors simultaneously — no single sensor gives complete information about the environment. The fused training signal only works when labels are consistent and time-aligned across all modalities: a 3D bounding box on a LiDAR point cloud that does not correspond to the same object identified in the camera frame teaches the model a contradiction rather than a perception capability. Specialist sensor-fusion annotation is the step that prevents those contradictions from entering the training data.
We annotate RGB cameras, depth sensors, LiDAR point clouds, IMU motion data, and force/torque readings. Annotation types include 3D bounding boxes and point-level segmentation on point clouds, semantic labels on RGB-D data, cross-sensor object correspondence, and event-level alignment across IMU and force/torque traces. The label taxonomy is built for each program’s specific sensor configuration and training objectives — not applied from a generic template — because the annotation requirements for a mobile manipulator differ significantly from those for a humanoid or an autonomous vehicle.
Standard video or image annotation labels one modality: objects detected in a camera frame, actions recognized in a video sequence. Sensor-fusion annotation must simultaneously label multiple modalities and preserve the cross-modal correspondence and timestamp coherence that makes the fusion work. It also requires 3D and point-cloud expertise that most annotation programs do not have: labeling a LiDAR point cloud with 3D bounding boxes is a fundamentally different skill from drawing bounding boxes on a camera image. Our annotators are trained specifically for multimodal 3D labeling, not repurposed from 2D annotation programs.
Yes. With 1,500+ trained specialists, SOC-compliant workflows, and delivery infrastructure that scales with your sensor configuration and data volume, we handle large multimodal datasets while maintaining accuracy, temporal alignment, and security across every modality. Multi-sensor programs often generate substantial data volumes quickly, particularly when running continuous deployment capture. Our managed-service model ingests new sensor data on a recurring cadence, labels it across all modalities in a synchronized workflow, and returns model-ready datasets without requiring a separate project setup for each batch.
Annotera provides the annotation layer. If your robotics program also requires human demonstration capture, teleoperation infrastructure, multimodal sensor collection, or sim-to-real data pipelines, Roborax can support the upstream data-generation workflow.
Roborax is Annotera’s sister brand under the Omind AI portfolio, purpose-built for robotics companies developing embodied AI systems.
