AtomMap: A probabilistic amorphous 3D map representation for robotics and surface reconstruction

Abstract

We present a new 3D probabilistic occupancy map representation for robotics applications by relaxing the commonly-assumed constraint that space must be perfectly tessellated. We replace the regular structure of 3D grids with an unstructured collection of non-overlapping, equally-sized spheres, which we call “atoms”. Abandoning the grid structure allows a more accurate representation of space directly tangent to surfaces, which facilitates a number of applications such as high fidelity surface reconstruction and surface-guided path planning. Maps composed of atoms can distinguish between free, occupied, and unknown space, support computationally efficient insertions and collision queries, provide free space planning guarantees, and achieve state-of-the-art memory efficiency over large volumes. This is achieved while simultaneously reducing quantization effects in the vicinity of surfaces and defining a useful implicit surface representation.