Object SLAM is considered increasingly significant for robot high-level
perception and decision-making. Existing studies fall short in terms of data
association, object representation, and semantic mapping and frequently rely on
additional assumptions, limiting their performance. In this paper, we present a
comprehensive object SLAM framework that focuses on object-based perception and
object-oriented robot tasks. First, we propose an ensemble data association
approach for associating objects in complicated conditions by incorporating
parametric and nonparametric statistic testing. In addition, we suggest an
outlier-robust centroid and scale estimation algorithm for modeling objects
based on the iForest and line alignment. Then a lightweight and object-oriented
map is represented by estimated general object models. Taking into
consideration the semantic invariance of objects, we convert the object map to
a topological map to provide semantic descriptors to enable multi-map matching.
Finally, we suggest an object-driven active exploration strategy to achieve
autonomous mapping in the grasping scenario. A range of public datasets and
real-world results in mapping, augmented reality, scene matching,
relocalization, and robotic manipulation have been used to evaluate the
proposed object SLAM framework for its efficient performance.

本文提出了一种综合的基于物品的感知和基于物品的机器人任务的物体 SLAM 框架，专注于解决 SLAM 中的数据关联、物体表示和语义地图问题，并通过公开数据集和真实世界结果对其性能进行了评估。

一个物体 SLAM 框架：关联、映射和高级任务

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

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.

本文提出一种名为 NeuSE 的神经 SE（3）等变嵌入，以支持对象 SLAM 实现长期场景变化下的一致空间理解，通过 partial object observations 生成一组潜在的物体嵌入，可以直接从推断的潜在代码中导出相对帧变换，利用 NeuSE 进行物体形状和姿态表征的 SLAM 范式，能够单独工作或与传统 SLAM 系统同时工作，并显示出改进的定位精度和可根据真实世界变化自适应的地图绘制能力，可以将其与常见的 SLAM 流水线集成。