摘要
机器人与机器学习每日新闻-机器人与自动化的最新研究结果已经发表。根据NewsRx编辑在英国泰恩河畔Ne Wcastle的新闻报道，研究称：“在这封信中，我们提出了一种基于WiFi测距测量的室内定位系统rWiFiSLAM。当移动机器人无法在室内环境中获得高质量的GPS信号时，室内定位技术在移动机器人中发挥着重要作用。”我们的新闻记者从纽卡斯尔大学的研究中获得了一句话：“室内定位还有许多其他应用，如救援、智能建筑等。惯性测量单元(IMU)已被用于行人D EAD推算(PDR)，以在室内环境中提供定位服务，但它不依赖于任何其他信号。尽管PDR是一种有前途的技术，它仍然受到移动设备imu不可避免的噪声和偏差的影响。在这些情况下，本振闭合是必要的。我们在一个鲁棒的位姿图SLAM框架中设计了一种基于WiFi测距测量和IMU测量的高效环路闭合机制，该方法的一个优点是去除了对WiFi接入点位置的全部知识要求，使我们提出的方法适用于新的和/或动态环境。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics - Ro botics and Automation have been published. According to news reporting out of Ne wcastle upon Tyne, United Kingdom, by NewsRx editors, research stated, “In this letter, we propose rWiFiSLAM, an indoor localisation system based on WiFi rangin g measurements. Indoor localisation techniques play an important role in mobile robots when they cannot access good quality GPS signals in indoor environments.” Our news journalists obtained a quote from the research from Newcastle Universit y, “Indoor localisation also has many other applications, such as rescue, smart buildings, etc. Inertial Measurement Units (IMU) have been used for Pedestrian D ead Reckoning (PDR) to provide localisation services in the indoor environment a s it does not rely on any other signals. Although PDR is a promising technique, it still suffers from unavoidable noise and bias from IMUs in mobile devices. Lo op closure is necessary for these scenarios. In this letter, we design an effici ent loop closure mechanism based on WiFi ranging measurements along with IMU mea surements in a robust pose graph SLAM framework for indoor localisation. One nov elty of the proposed method is that we remove the requirement of the full knowle dge of the WiFi access point locations, which makes our proposed method feasible for new and/or dynamic environments.”
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