An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions

TL;DR

This paper introduces a novel algorithm enabling remapping of neural implicit maps in SLAM systems, allowing for loop closure correction and improved 3D reconstruction quality.

Contribution

It presents a transformable neural implicit map that supports remapping, addressing a key limitation of previous neural implicit mapping methods.

Findings

Remapping neural implicit maps is feasible with the proposed algorithm.

The method improves loop closure handling in SLAM.

High-quality surface reconstruction is achieved after remapping.

Abstract

Implicit representations are widely used for object reconstruction due to their efficiency and flexibility. In 2021, a novel structure named neural implicit map has been invented for incremental reconstruction. A neural implicit map alleviates the problem of inefficient memory cost of previous online 3D dense reconstruction while producing better quality. % However, the neural implicit map suffers the limitation that it does not support remapping as the frames of scans are encoded into a deep prior after generating the neural implicit map. This means, that neither this generation process is invertible, nor a deep prior is transformable. The non-remappable property makes it not possible to apply loop-closure techniques. % We present a neural implicit map based transformation algorithm to fill this gap. As our neural implicit map is transformable, our model supports remapping for this special map of latent features. % Experiments show that our remapping module is capable to well-transform neural implicit maps to new poses. Embedded into a SLAM framework, our mapping model is able to tackle the remapping of loop closures and demonstrates high-quality surface reconstruction. % Our implementation is available at github\footnote{\url{https://github.com/Jarrome/IMT_Mapping}} for the research community.