Fusion starts before the algorithm
Camera, thermal, event and inertial sensors observe the world through different sampling models. A fusion algorithm can only combine them correctly when the electronics preserve when each observation was made and how the sensors are positioned relative to one another.
Build a shared time model
Hardware triggers, disciplined clocks and ingress timestamps each solve different parts of the problem. The architecture should define clock sources, synchronization error, timestamp location and behavior after restart or link interruption. Measuring time uncertainty is more useful than assuming every interface is synchronized.
Treat calibration as managed configuration
Intrinsic calibration describes an individual sensor; extrinsic calibration relates sensors to each other and to the platform. Temperature, mechanical service and replacement can change those relationships. Calibration records therefore need identifiers, versions and validity checks that travel with the data.
Partition workloads by latency and context
Deterministic preprocessing and timestamp alignment may belong in an FPGA or real-time core, while feature extraction and inference run on an NPU or application processor. Keeping raw evidence, health data and selected intermediate results makes fusion decisions observable rather than opaque.
| Architecture layer | Responsibility |
|---|---|
| Sensor boundary | Capture, trigger and timestamp close to acquisition. |
| Calibration | Maintain intrinsic, extrinsic and timing relationships. |
| Compute partition | Place deterministic and AI workloads on suitable resources. |
| Evidence trail | Retain health, timing quality and decision context. |
What camera, LiDAR and IMU measurements contribute
A visible-light camera supplies appearance information for computer vision. LiDAR measures scene geometry through range observations. An inertial measurement unit measures motion-related quantities such as angular rate and acceleration. These sources have different rates, noise characteristics and failure modes; combining them requires a model of those differences.
Select sensors against the operating environment and the estimate you need: object position, machine alignment or robot motion. More sensors can increase integration cost without improving the decision. Start with a measurable failure case for the existing sensing approach, then evaluate whether another modality resolves it.
Budget capture time, transport delay and processing latency
Two messages arriving together may describe different moments. Preserve capture timestamps where available and document how device clocks relate to the common clock. Transport buffering, exposure and inference delay belong in the timing budget. Test moving objects and turning platforms because static scenes can hide synchronization errors.
Extrinsic calibration describes the relative position and orientation of sensors. Store it with the sensor configuration and software revision. A moved camera or a replacement bracket can invalidate a previously useful transformation; define how installation changes trigger a calibration check.
Use sensor fusion in robotics and localization
For an autonomous mobile robot, perception and localization have related but different outputs. An obstacle estimate may feed local planning, while LiDAR, camera, wheel odometry and inertial data can support localization or SLAM, depending on the chosen architecture. Define coordinate frames, uncertainty and maximum data age at each interface.
ROS 2 provides integration tools, but message compatibility alone does not establish a robust robot. Validate sensor dropout, delayed packets and degraded lighting in the intended installation. Platform and driver compatibility must be confirmed for the particular project.
Read the robotics perception and control guide
Technical reference: Nav2: sensor sources and navigation messages
Apply the architecture to a real platform.
Share the interfaces, environment, data rates and decision metric. We will map the engineering trade-offs to a practical system configuration.
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