Perception Stack Blog
Perception, from sensor to estimate
Technical articles and worked tutorials on sensor hardware, deep-learning mathematics, and navigation systems, connected as one perception pipeline.
Three pillars
Three domains, one pipeline
Hardware sensors
Cameras, IMUs, and lidar: what each sensor measures, its noise characteristics, and calibration.
Deep-learning math
Derivations behind neural networks, built step by step from foundational definitions.
Navigation systems
Localization, mapping, and state estimation architectures for autonomous platforms.
Our approach
Start with the measurement
Every article begins with what the sensor actually measures, then works outward through the filters, models, and assumptions that turn raw data into a state estimate. Tutorials make each step explicit, so you can see where an idea depends on the hardware.
Worked examples, explicit assumptions, and clear trade-offs, written for engineers who want the mental model as well as the equations.
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