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.

翻译：我们提出NeuSE——一种新颖的物体神经SE(3)等变嵌入方法，并阐述其如何支持面向长期场景变化的物体SLAM实现一致性空间理解。NeuSE是基于部分物体观测创建的一组潜在物体嵌入，可作为完整物体模型的紧凑点云替代，在编码完整形状信息的同时，随物理世界中的物体同步进行SE(3)等变变换。通过NeuSE，可直接从推断的潜在编码中推导出相对帧变换。我们提出的SLAM范式利用NeuSE进行物体形状与位姿表征，既可独立运行，也可与典型SLAM系统协同工作。该范式直接推断与通用SLAM位姿图优化兼容的SE(3)相机位姿约束，同时维护适应真实世界变化的轻量级以物体为中心的地图。我们在包含物体变化的合成与真实世界序列上评估该方法，结果表明无论独立运行还是与通用SLAM管线协同工作，该方法均能提升定位精度并具备变化感知建图能力。