Point (geometry)
Related papers: 20
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A point, in the geometric sense, is a fundamental mathematical primitive representing an exact location in space with no dimension — defined solely by its coordinates. In robotics and AI, points serve as the atomic unit of spatial representation: collections of points (called point clouds) captured by sensors like LiDAR or depth cameras describe the 3D geometry of objects and environments. These point clouds enable robots to perceive, map, and interact with the physical world — supporting tasks such as object recognition, grasp planning, simultaneous localization and mapping (SLAM), and scene reconstruction. Deep learning architectures applied directly to point clouds allow robots to classify shapes, detect obstacles, register successive scans, and complete partial observations. Beyond perception, points appear in planning contexts, such as belief-point approximations in POMDP solvers and zero-moment point calculations for legged robot stability. The concept matters because virtually every spatial computation in robotics — from path following to manipulation — ultimately reduces to reasoning about locations and relationships between points in 2D or 3D space.
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