摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于机器人的新报告。根据NewsR X记者在伊利诺伊州香槟的新闻报道，研究表明：“移动机器人要成功地部署在未受限制的领域，就需要了解环境和地形，以避免危险区域、卡住和与障碍物相撞。可穿越性和刺激-预测机器人可以在环境中旅行的地方-是解决这个问题的首要方法。”这项研究的财政支持来自国家机器人倡议2.0.新闻记者从伊利诺伊大学的研究中获得了一句话：“现有的几何方法可能忽略了重要的语义考虑，而语义分割方法涉及繁琐的标注过程。自监督方法减少了标注的繁琐，但需要额外的模型数据，并且往往难以明确地标注不可穿越的区域。为了弥补这些局限性，本文提出了一种弱监督的相对跟踪性估计方法，该方法通过人工标注少量点对的相对跟踪性，与传统的基于分割的方法相比，大大减少了标记工作量，避免了自监督方法的局限性，并通过一种新的交叉图像标记策略和损失函数进一步提高了算法的性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ro botics. According to news reporting originating in Champaign, Illinois, by NewsR x journalists, research stated, “Successful deployment of mobile robots in unstr uctured domains requires an understanding of the environment and terrain to avoi d hazardous areas, getting stuck, and colliding with obstacles. Traversability e stimation-which predicts where in the environment a robot can travel-is one prom inent approach that tackles this problem.” Financial support for this research came from National Robotics Initiative 2.0. The news reporters obtained a quote from the research from the University of Ill inois, “Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Rece nt self-supervised methods reduce labeling tedium, but require additional data o r models and tend to struggle to explicitly label untraversable areas. To addres s these limitations, we introduce a weakly-supervised method for relative traver sability estimation. Our method involves manually annotating the relative traver sability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitat ions of self-supervised methods. We further improve the performance of our metho d through a novel cross-image labeling strategy and loss function.”
NETL
NSTL