Abstract
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. However, utilizing such objects to localize the robot and build an open-set semantic map of the world remains an open research question. In this work, a system of identifying, localizing, and encoding objects is tightly coupled with probabilistic graphical models for performing open-set semantic simultaneous localization and mapping (SLAM). Results are presented demonstrating that the proposed lightweight object encoding can be used to perform more accurate object-based SLAM than existing open-set methods, closed-set methods, and geometric methods while incurring a lower computational overhead than existing open-set mapping methods.
Abstract (translated)
使机器理解世界的对象是实现更高层次自主的关键构建模块。基础模型在视觉上的成功已经使人们能够将近世界中的几乎所有物体进行分割和识别。然而，将这样的物体用于定位机器人并构建一个开放集语义图仍然是一个开放的研究问题。在这项工作中，一个用于识别、定位和编码物体的系统与用于进行开放集语义同时定位和映射（SLAM）的概率图模型紧密耦合。结果表明，与现有开放集方法和封闭集方法相比，所提出的轻量级物体编码可以在更准确的基础上进行物体为基础的SLAM，同时计算开销更低。
URL
https://arxiv.org/abs/2404.04377
