This invention provides a calibration-free
path reconstruction method for buried cables based on Topo-NeRF and
active sensing. Addressing the issues of traditional
magnetic field inversion relying on calibration and being prone to mismatch in environments with strong interference such as
reinforced concrete, the method preprocesses
raw data to obtain a spatiotemporal
feature matrix. This matrix undergoes
feature extraction, spatial alignment, and drift compensation to eliminate extrinsic parameter calibration, resulting in multi-source fused data. This multi-source fused data is combined to characterize the cable's
magnetic field using a continuous three-dimensional topological manifold. An implicit neural
radiation field is constructed and coupled with differentiable electromagnetic rendering. The cable body and
branch nodes are separated within a persistently cohomologically constrained topological
bottleneck layer. The initial reconstruction results are combined with conditional
diffusion and ground-penetrating
radar dielectric priors to enhance low
signal-to-
noise ratio weak fields, ensuring consistency between the reconstruction results and the underground
physical structure. Based on reconstruction uncertainties, topological entropy is calculated, and online acquisition trajectories are planned to form a closed-loop
active sensing system. Finally, a three-dimensional path model is output, enabling
rapid detection of underground cables.