perception

A robot is only as good as what it can measure. Explore the full sensor toolkit — encoders, IMUs, ultrasonic rangefinders, LiDAR, cameras, and force/torque sensors — and learn the four metrics that determine whether a sensor is fit for purpose: resolution, range, noise, and sampling rate.

How LiDAR fires pulses and measures time-of-flight to build a 3D point cloud; data structures, voxel downsampling, ICP registration, ground segmentation, and an honest comparison with cameras for robot perception.

From photons to 3D geometry: the pinhole model, intrinsic matrix K, lens distortion, feature matching, stereo depth, and where CNNs help (and fail) in robot perception pipelines.

What "multimodal" actually means once you get past marketing copy. How modern models like GPT-4o, Gemini, and Claude blend modalities, and the design trade-offs (early vs late fusion, native vs adapted) you'll meet when building with them.