Accurate Implicit Neural Mapping with More Compact Representation in Large-scale Scenes Using Ranging Data

Chenhui Shi, Fulin Tang, Yihong Wu, Xin Jin, Gang Ma

Abstract:

Large-scale 3D mapping nowadays is a research
hotspot in robotics. A greatly concerning issue is reconstructing
high-accuracy maps in a hardware environment with limited
memory. To address this problem, we propose a novel implicit
neural mapping approach with higher accuracy and
less memory. It first adopts an improved hierarchical hash
encoder, independent of geometric bounding (e.g., bounding
box or sphere), for a more compact map representation, and
then leverages a spatial hash grid to restrict the encoding
space to the proximity of geometric surfaces, preventing hash
collisions between encoding in free space and near geometric
surfaces. The hash grid indexes the scene point cloud produced
by ranging data. Through a tiny MLP, features encoded from
sampled points in the hash grid can be converted to truncated
signed distance values. To further improve mapping accuracy,
a new method is developed to instantly obtain more accurate
signed distance labels from ranging data by computing the
closest distances from sampled points to the point cloud indexed
by the constructed hash grid, not just the distances from
sampled points to geometric surfaces along rays, and then
use these labels to supervise the learning of our hash encoder.
Experimental evaluations performed on large-scale indoor and
outdoor datasets demonstrate that our approach achieves stateof-
the-art mapping performance with less than half of the
memory consumption compared with previous advanced 3D
mapping methods using ranging data.