Semantic segmentation based clustering

This invention describes a system that uses radar data to identify separate objects in an environment. It works by first taking raw radar measurements, where each point is assigned a meaningful category by a machine-learning model. Then, it groups these categorized points together using a DBSCAN algorithm to form distinct clusters, each representing a different object.

Why it matters: The core of this invention relies on a machine-learning model for semantic labeling. Since 2021, advancements in ML architectures and readily available pre-trained models have significantly lowered the barrier to developing and deploying robust semantic segmentation for point cloud data, making the ML component more accessible and performant.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedMachine learning model, software logic

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