See how the Tangram Vision Platform can radically accelerate your perception roadmap.
Musings on perception, sensors, robotics, autonomy, and vision-enabled industries
Why are cameras, LiDAR, and depth sensors so popular with roboticists and autonomy engineers?
We discuss event cameras, one of our favorite up-and-coming modalities in the Sensoria Obscura of autonomy.
Why are autonomy companies experimenting with (and increasingly adopting) thermal cameras as part of their sensor arrays?
Follow as we go through a common recipe we use to automatically regenerate our internal Docker image whenever rust:latest is updated.
How do fiducial markers work, and what makes a great fiducial marker?
HDR cameras can be useful for scenarios where lighting conditions can change drastically. But they come with challenges.
It can be a pain to set up static websites by hand with S3. We can automate the process with Terraform.
Everyone wants to know about calibration accuracy. What they should really be asking about is calibration precision.
Now that Intel is shutting down RealSense, what should you do if you use their sensors?
The Tangram Vision team takes its best stab at guessing what goes into the FarmWise FT35's sensor array
There are two primary lens distortion models to provide correction. We'll go over these, and dive into the math and approach.
In this series, we explore another part of the camera modeling process: modeling lens distortions.
Learn about solid state and scanning LiDARs, as well as what models are available now for prototyping and deployment.
What do we do when our perception pipeline explodes? Easy: bring in a perception plumber.
We explore one of the fundamental aspects of the calibration problem: choosing a model
Access code building blocks to create a sensor calibration module in Rust.
The Tangram Vision Platform lets perception teams develop and deploy faster.
