Abstract
For autonomous robotics applications, it is crucial that robots are able to accurately measure their potential state and perceive their environment, including other agents within it (e.g., cobots interacting with humans). The redundancy of these measurements is important, as it allows for planning and execution of recovery protocols in the event of sensor failure or external disturbances. Visual estimation can provide this redundancy through the use of low-cost sensors and server as a standalone source of proprioception when no encoder-based sensing is available. Therefore, we estimate the configuration of the robot jointly with its pose, which provides a complete spatial understanding of the observed robot. We present GISR - a method for deep configuration and robot-to-camera pose estimation that prioritizes real-time execution. GISR is comprised of two modules: (i) a geometric initialization module, efficiently computing an approximate robot pose and configuration, and (ii) an iterative silhouette-based refinement module that refines the initial solution in only a few iterations. We evaluate our method on a publicly available dataset and show that GISR performs competitively with existing state-of-the-art approaches, while being significantly faster compared to existing methods of the same class. Our code is available at this https URL.
Abstract (translated)
对于自主机器人应用，确保机器人能够准确测量其潜在状态并感知其环境（包括其内部的其他机器人，例如与人类交互的协作机器人），对冗余进行重要评估，以便在传感器故障或外部干扰的情况下执行恢复协议。视觉估计可以通过使用低成本传感器和服务器作为自包含姿态感觉器时提供冗余来实现。因此，我们与姿态一起估计机器人的配置，这提供了对观察到的机器人的完整空间理解。我们提出了GISR - 一个注重实时执行的机器人配置和机器人-相机姿态估计方法。GISR由两个模块组成：（i）一个几何初始化模块，高效计算出近似的机器人姿态和配置；（ii）一个迭代轮廓基于平滑的优化模块，仅在几次迭代后对初始解决方案进行平滑。我们在公开可用的数据集上评估我们的方法，并证明了GISR与现有高级方法具有竞争力，同时比相同类型的现有方法速度更快。我们的代码可在此处访问：https://www.thorlabs.com/newgrouppage9.cfm?objectgroup_id=11375
URL
https://arxiv.org/abs/2405.04890
