Object encoding and identification is crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but difficult to recall revisited objects precisely. In this paper, we propose a novel object encoding method based on a graph of key-points. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the art algorithm and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in the robotic applications.

中文翻译：

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鲁棒的对象编码方法

对象编码和识别对于许多机器人任务至关重要，例如自主探索和语义重新定位。现有作品在很大程度上依赖于对检测到的对象的跟踪，但是很难准确地回忆起重新访问的对象。在本文中，我们提出了一种基于关键点图的新型对象编码方法。为了对检测到的关键点数量保持鲁棒性，我们提出了一种特征稀疏编码和对象密集型编码方法，以确保每个关键点只能影响对象描述符的一小部分，从而使其对视点更改，缩放，咬合，甚至物体变形。在实验中，我们表明，与最新算法相比，它在对象识别方面具有更高的性能，并且能够提供可靠的语义重新定位。