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NewsRx COR的受访者《来自捷克共和国布拉格的新闻》报道，研究表明：“移动机器人状态估计中使用的典型点云采样方法保持了高度的点冗余。冗余不必要地减慢了估计流水线，并可能导致实时约束下的DRI FT。”这项研究的财政支持来自CTU。我们的新闻记者从捷克技术大学的研究中获得了一句话：“这种过度的延迟成为资源受限的机器人（特别是无人机）的瓶颈，需要最小的延迟来实现敏捷和精确的操作。我们提出了一种新颖的、确定的、不知情的、单参数的点云采样方法RMS，它最大限度地减少了三维点云中的冗余。”RMS利用线性和平面表面固有的高冗余度这一事实平衡了平移空间的可观测性,并将其传播到迭代估计管道中。本文将均方根与基于点的KISS-ICP和基于特征的LOAM里程测量管道相结合，在KITI、HILTI-Oxford和多旋翼无人机定制数据集上进行了实验评估。
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 originating from Prague, Czech Republic, by NewsRx cor respondents, research stated, “The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy. Th is redundancy unnecessarily slows down the estimation pipeline and may cause dri ft under real-time constraints.” Financial support for this research came from CTU. Our news journalists obtained a quote from the research from Czech Technical Uni versity, “Such undue latency becomes a bottleneck for resource-constrained robot s (especially UAVs), requiring minimal delay for agile and accurate operation. W e propose a novel, deterministic, uninformed, and single-parameter point cloud s ampling method named RMS that minimizes redundancy within a 3D point cloud. In c ontrast to the state of the art, RMS balances the translation-space observabilit y by leveraging the fact that linear and planar surfaces inherently exhibit high redundancy propagated into iterative estimation pipelines. We define the concep t of gradient flow, quantifying the local surface underlying a point. We also sh ow that maximizing the entropy of the gradient flow minimizes point redundancy f or robot egomotion estimation. We integrate RMS into the point-based KISS-ICP a nd feature-based LOAM odometry pipelines and evaluate experimentally on KITTI, H ilti-Oxford, and custom datasets from multirotor UAVs.”
NETL
NSTL