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.

翻译：物体SLAM被认为对机器人高层感知与决策日益重要。现有研究在数据关联、物体表示及语义建图方面存在不足，且通常依赖额外假设，限制了其性能。本文提出一个全面的物体SLAM框架，专注于基于物体的感知和面向物体的机器人任务。首先，我们提出一种集成数据关联方法，通过结合参数与非参数统计检验，在复杂条件下关联物体。此外，我们基于iForest和线对齐提出一种鲁棒离群点的质心与尺度估计算法，用于建模物体。随后，通过估计的通用物体模型表示一种轻量级且面向物体的地图。考虑物体的语义不变性，我们将物体地图转换为拓扑地图，以提供语义描述符实现多地图匹配。最后，我们提出一种物体驱动的主动探索策略，在抓取场景中实现自主建图。通过一系列公开数据集以及在建图、增强现实、场景匹配、重定位和机器人操作中的实际结果，对所提出的物体SLAM框架的高效性能进行了评估。