Engineering note
Event-Based vs Frame-Based Vision: Choosing for the Decision Loop
Compare event-based and frame-based vision by latency, data behavior, image content and integration trade-offs for autonomous edge systems.
Read the note →Fused edge perception
Multi-Sensor Fusion for Robust Edge Perception
A synchronized processing platform that combines complementary sensors so autonomous and industrial systems can maintain awareness when any single modality is limited.
No single sensor performs perfectly in every environment. Visible cameras can struggle with glare or darkness, thermal sensors provide different contrast, event sensors excel at motion, and inertial measurements preserve short-term dynamics when visual features disappear. Fusion works when the electronics, timestamps, calibration and software are designed together.
Alvexis develops carrier electronics and processing pipelines that bring these inputs into a common time base. The resulting platform supports synchronized capture, calibration, feature-level fusion and application-specific inference close to the sensors.
System architecture
1
Sensor inputs
Event, frame, thermal, inertial and customer-specific sources.
2
Synchronization
Hardware triggers, shared clocks and timestamp alignment.
3
Calibration
Intrinsic, extrinsic and timing calibration management.
4
Fusion
Feature, track or decision-level fusion on embedded compute.
Capabilities
Engineering scope
- Multiple camera and sensor interface options
- Hardware trigger and timestamp distribution
- Calibration storage and configuration control
- FPGA-assisted data conditioning
- Embedded Linux or RTOS partitioning
- Recorded datasets for algorithm validation
Applications
Typical use cases
- Navigation in degraded visual conditions
- Multi-modal object detection
- Autonomous payload development
- Industrial inspection
- Research data acquisition
- Sensor-algorithm co-design
Specification discipline
What is defined during a pilot.
Values depend on the selected sensors, processor, interfaces, environment and acceptance method.
| Area | Definition |
|---|---|
| Sensor set | Selected around the mission environment and failure modes. |
| Synchronization accuracy | Specified after interface and clock architecture definition. |
| Compute partition | FPGA, NPU and MPU workloads are assigned from latency and power needs. |
| Program status | Custom platform development; interfaces and mechanical envelope are program specific. |
Related
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Perception Systems
ChronaVision
Event-based vision hardware for low-latency motion perception, FPGA preprocessing and local AI inference in autonomous and industrial systems.
Explore system →Define the multi-sensor fusion system configuration.
Send the interfaces, environment, decision metric and pilot quantity. We will identify the architecture and validation plan.