With the aim of bridging the gap between high quality reconstruction and
mobile robot motion planning, we propose an efficient system that leverages the
concept of adaptive-resolution volumetric mapping, which naturally integrates
with the hierarchical decomposition of space in an octree data structure.
Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of
occupancy probabilities in log-odds representation, which allows to represent
both surfaces, as well as the entire free, i.e. observed space, as opposed to
unobserved space. We introduce a method for choosing resolution -- on the fly
-- in real-time by means of a multi-scale max-min pooling of the input depth
image. The notion of explicit free space mapping paired with the spatial
hierarchy in the data structure, as well as map resolution, allows for
collision queries, as needed for robot motion planning, at unprecedented speed.
We quantitatively evaluate mapping accuracy, memory, runtime performance, and
planning performance showing improvements over the state of the art,
particularly in cases requiring high resolution maps.