Abstract
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.
Abstract (translated)
我们提出了 NeuSE 对象特性编码方案,一种全新的 Neural SE(3)-Equivariant Embedding 方法,用于实现对象 SLAM,并展示它如何支持对象在长期场景变化中 consistent 空间理解。NeuSE 是从部分对象观测中提取的隐含对象Embeddings,充当完整对象模型的紧凑点云模拟,同时与现实世界的对象协同编码 full 形状信息,实现 SE(3)-equivariant 变换。与 NeuSE 配合使用,可以直接从推断的隐含编码中推导出相对帧变换。我们提出的 SLAM 范式,使用 NeuSE 对对象形状和姿态进行特征化,可以独立运行或与典型 SLAM 系统协同工作。它直接推断与一般 SLAM 姿态图优化兼容的 SE(3)相机姿态限制,同时保持轻量级的对象中心地图,适应现实世界的变化。我们的方法在模拟和实际场景中进行了验证,展示了在单独运行或与通用 SLAM 流程共同工作时提高定位精度和变化感知能力的能力。
URL
https://arxiv.org/abs/2303.07308
