LGSDF: Continual Global Learning of Signed Distance Fields Aided by
  Local Updating
Yufeng Yue Yinan Deng Jiahui Wang Yi Yang
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves
training a neural network to regress the signed distance from any point to the
nearest obstacle, which has the advantages of lightweight storage and
continuous querying. However, existing algorithms usually rely on conflicting
raw observations as training data, resulting in poor map performance. In this
paper, we propose LGSDF, an ESDF continual Global learning algorithm aided by
Local updating. At the front end, axis-aligned grids are dynamically updated by
pre-processed sensor observations, where incremental fusion alleviates
estimation error caused by limited viewing directions. At the back end, a
randomly initialized implicit ESDF neural network performs continual
self-supervised learning guided by these grids to generate smooth and
continuous maps. The results on multiple scenes show that LGSDF can construct
more accurate ESDF maps and meshes compared with SOTA (State Of The Art)
explicit and implicit mapping algorithms. The source code of LGSDF is publicly
available at https://github.com/BIT-DYN/LGSDF.