NeuSE: 物体の一貫した空間理解のためのニューラルSE(3)同変埋め込み
NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects
物体の部分観測からSE(3)同変な潜在埋め込みを生成し、物体SLAMにおける長期シーン変化に対応した一貫した空間理解と変化認識マッピングを実現する手法を提案。
著者: Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum, John J. Leonard
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding full shape information while transforming SE(3)-equivariantly in tandem with the object in the physical world. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while also maintaining a lightweight object-centric map that adapts to real-world changes. Our approach is evaluated on synthetic and real-world sequences featuring changed objects and shows improved localization accuracy and change-aware mapping capability, when working either standalone or jointly with a common SLAM pipeline.
