Grid
Related papers: 20
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A grid is a discrete spatial data structure that divides an environment into regular, uniform cells — typically squares in 2D or voxels in 3D — each storing information about that region of space. In robotics and AI, grids are foundational tools for representing, reasoning about, and navigating the physical world. Occupancy grids, one of the most prevalent forms, assign probabilistic values to each cell indicating whether it is free, occupied, or unknown, built incrementally from sensor data such as LiDAR or depth cameras. This representation directly supports simultaneous localization and mapping (SLAM), where robots must build a map while tracking their own position within it. Grids also underpin path planning algorithms like A*, Theta*, and Field D*, which search through cell networks to compute collision-free routes. Their regular structure makes them compatible with convolutional neural networks for learning-based perception and decision-making. Grids matter because they offer a principled, scalable bridge between raw sensor measurements and actionable spatial reasoning, enabling robots to reliably perceive, map, localize, and navigate complex real-world environments.
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