Perception
Sparse Optical Flow
Sparse optical flow is the estimation of apparent image motion at a selected set of feature points across consecutive frames, rather than at every pixel as in dense optical flow. The Lucas-Kanade method with pyramidal refinement, applied to corners from detectors like Shi-Tomasi, is the classical pipeline. Sparse flow is computationally cheap and underlies feature tracking in visual odometry and visual-inertial SLAM front ends.
Why it matters for physical AI
Feature tracking at kilohertz-friendly cost keeps visual odometry running on embedded compute, anchoring ego-motion estimates for drones and mobile manipulators without GPUs.
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