N^3-Mapping: Normal Guided Neural Non-Projective Signed Distance Fields for Large-scale 3D Mapping
CoRR(2024)
摘要
Accurate and dense mapping in large-scale environments is essential for
various robot applications. Recently, implicit neural signed distance fields
(SDFs) have shown promising advances in this task. However, most existing
approaches employ projective distances from range data as SDF supervision,
introducing approximation errors and thus degrading the mapping quality. To
address this problem, we introduce N3-Mapping, an implicit neural mapping
system featuring normal-guided neural non-projective signed distance fields.
Specifically, we directly sample points along the surface normal, instead of
the ray, to obtain more accurate non-projective distance values from range
data. Then these distance values are used as supervision to train the implicit
map. For large-scale mapping, we apply a voxel-oriented sliding window
mechanism to alleviate the forgetting issue with a bounded memory footprint.
Besides, considering the uneven distribution of measured point clouds, a
hierarchical sampling strategy is designed to improve training efficiency.
Experiments demonstrate that our method effectively mitigates SDF approximation
errors and achieves state-of-the-art mapping quality compared to existing
approaches.
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