SPLEENLAB®
Spleenlab OS
Spleenlab OS simplifies running and evaluating applications on edge devices by providing integrated tools for visualization, optimization, and data recording through the Spleenlab Suite.
Why Spleenlab OS?
Spleenlab OS eliminates the typical complexity of working with edge devices by offering a ready-to-use suite that lets users monitor, analyze, and store performance data without additional engineering effort.
Instead of building custom tools from scratch, teams can access powerful diagnostics and control features directly through a web browser—saving time, reducing costs, and accelerating deployment.
No custom development needed
Web-based access
Faster deployment
See how it works
Benefits
Universal Compatibility
Runs seamlessly on any edge device, ensuring broad hardware support without extra configuration.
Flexible Integration
Adapts effortlessly to Spleenlab’s supported ROS2 versions and applications for smooth deployment.
Lightweight Design
Engineered with a small footprint to maximize performance even on resource-constrained systems.
Features
Unlock the full potential of Spleenlab OS with these advanced features, engineered to deliver real-time insights, robust performance tracking, and seamless data handling for autonomous systems:
Data Recording - Seamlessly captures and stores all relevant operational data for future analysis.
Application Monitoring - Continuously tracks application performance and health in real time.
Live Data Evaluation - Analyzes incoming data instantly to support quick decision-making.
Data Streaming - Enables efficient and continuous flow of data across systems and components.
Performance Measuring - Accurately assesses system and application performance metrics to optimize efficiency.
These performance metrics are for demonstrative purposes only, based on configurations with proven results. Actual performance may vary by setup. Our algorithms are optimized for use with any chip, platform, or sensor. Contact us for details.
Update Rate
5-10 Hz
Initialization Time
<10 seconds
Approach Accuracy
±0,2m (depending on the scene)
Target speed (for moving target)
Up to 10km/h
Operating Range
Line-of-sight or sensor-limited
Latency
<100 ms
Supported companion hardware
Nvidia Jetson, ModalAI Voxl2 / Mini, Qualcomm RB5, IMX7, IMX8, Raspberry PI
Supported flight controllers
PX4, APX4, Ardupilot
Basis-SW/OS
Linux, Docker required
Interfaces
ROS2 or Mavlink
Input - Sensors
Any type of camera (sensor agnostic)
Input - Data
• Camera's video frames
• Aerial vehicle’s odometry
• Aerial vehicle’s current flight height
• Intrinsic & extrinsic sensor calibration
Output - Data
• Navigation of the Aerial vehicle
• Position commands for the Autopilot
• Velocity and orientation commands for the Autopilot
Minimum
Recommended
RAM
2 GB
4 GB
Storage
20 GB
50 GB
Camera
640 x 480 px, 10 FPS
1920 x 1080 px, 30 FPS
IMU
100 Hz or GPS
300 Hz or GPS
The information provided reflects recommended hardware specifications based on insights gained from successful customer projects and integrations. These recommendations are not limitations, and actual requirements may vary depending on the specific configuration.
Our algorithms are compatible with any chip, platform, sensor, and individual configuration. Please contact us for further information.
Performance Metrics
Position accuracy
±2.5 cm in typical environments
update rate
Up to 200 Hz
initialization time
<1 second
Maximum Velocity
20 m/s with full accuracy
Operating Range
Unlimited (environment-dependent)
Drift
<0.1% of distance traveled
