Sensor Fusion
Encord Computer Vision Glossary
What Is Sensor Fusion?
Sensor fusion is the process of integrating data from multiple sensors to produce a unified environmental model that's more accurate and reliable than any individual sensor could provide alone. In physical AI systems, this typically means combining camera images, LiDAR point clouds, and radar returns — along with IMU and GPS data, into a coherent representation that the rest of the perception stack can reason over.
The goal isn't just to aggregate data. It's to resolve the gaps and ambiguities that each sensor leaves on its own, producing a world model that the system can trust.
How Sensor Fusion Works
Sensor fusion happens at multiple levels of the perception pipeline. Early fusion combines raw sensor data before any processing, fusing LiDAR point clouds with camera pixels, for instance, so the model learns directly from the combined input. Late fusion runs each sensor through its own processing pipeline first, then merges the outputs, combining a camera-based detection with a LiDAR-based detection of the same object to produce a single higher-confidence result.
Most production systems use a hybrid approach, fusing at multiple levels depending on the task and available compute.
Why It Matters for Training Data
Fused perception requires fused annotations. A 3D bounding box that exists in LiDAR space needs to be consistently projected into the corresponding camera frames. Labels on a camera image need to reference the same object as labels in the point cloud. Temporal alignment, making sure data from different sensors is synchronized to the same moment in time, is a prerequisite for any of this to work.
Inconsistent cross-sensor annotation is one of the most common sources of quality problems in AV and robotics datasets. If the LiDAR label and the camera label disagree on where an object is, the model learns conflicting information, and the errors show up in deployment.
Encord for Sensor Fusion Annotation
Encord's platform is built for multimodal annotation from the ground up, supporting simultaneous labeling across camera, LiDAR, and radar streams with cross-sensor object linking and temporal alignment tools. Annotators work in a unified workspace rather than separate tools for each modality, which keeps labels consistent across sensor views and reduces the cross-sensor alignment errors that degrade model performance downstream.
→ Explore Encord for Physical AI
→ Explore Annotation & Labeling
Related Terms
See also: LiDAR · Point Cloud · 3D Bounding Box / Cuboid · Bird's Eye View (BEV) · Autonomous Vehicle (AV) · ADAS · Object Detection
Related Resources
Informational Guides:
Technical Documentations:
Webinars and video content:
Frequently Asked Questions
Q1: Why not just use cameras? They're cheap and high-resolution.
Cameras are powerful but fragile. They degrade in low light, glare, rain, and fog, exactly the conditions where reliable perception matters most. LiDAR and radar fill those gaps. Most production AV systems use all three because the failure modes of each are complementary.
Q2: What's the biggest challenge in sensor fusion annotation?
Temporal and spatial alignment. Sensors capture data at different rates and from slightly different positions on the vehicle. Annotation needs to account for this, a LiDAR scan and a camera frame captured milliseconds apart may show the same object in slightly different positions. Getting this right requires precise calibration data and careful annotation tooling.
Q3: Is sensor fusion used in robotics as well as AVs?
Yes. Any physical AI system that needs to understand 3D space reliably uses some form of sensor fusion. Humanoid robots combine RGB cameras with depth sensors. Drones fuse cameras with IMU and GPS. Industrial robots combine vision with force-torque sensing. The specific modalities differ, but the principle is the same.
