论文标题
基于对象猛击的主动映射和机器人握把
Object SLAM-Based Active Mapping and Robotic Grasping
论文作者
论文摘要
本文介绍了复杂的机器人操作和自主感知任务的第一个主动对象映射框架。该框架是建立在与对机器人握把优化的同时多对象姿势估计过程集成的对象猛击系统上的。为了减少目标对象的观察不确定性并提高其姿势估计精度，我们还设计了一种以对象驱动的探索策略来指导对象映射过程，从而实现自主映射和高级感知。可以生成与机器人抓握兼容的准确对象图，结合了映射模块和勘探策略。此外，定量评估还表明，所提出的框架具有很高的映射精度。进行操作（包括对象抓握和放置）和增强现实的实验显着证明了我们提出的框架的有效性和优势。
This paper presents the first active object mapping framework for complex robotic manipulation and autonomous perception tasks. The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping. Aiming to reduce the observation uncertainty on target objects and increase their pose estimation accuracy, we also design an object-driven exploration strategy to guide the object mapping process, enabling autonomous mapping and high-level perception. Combining the mapping module and the exploration strategy, an accurate object map that is compatible with robotic grasping can be generated. Additionally, quantitative evaluations also indicate that the proposed framework has a very high mapping accuracy. Experiments with manipulation (including object grasping and placement) and augmented reality significantly demonstrate the effectiveness and advantages of our proposed framework.
