SLAM的全称为Simultaneous Localization And Mapping即“同时定位与地图构建”。最近几年由于机器人、无人机、自动驾驶、AI、VR和AR技术的发展,SLAM技术逐渐被人们熟知。SLAM技术是通过激光传感器感知周围的环境,并将不同时刻感知的...SLAM的全称为Simultaneous Localization And Mapping即“同时定位与地图构建”。最近几年由于机器人、无人机、自动驾驶、AI、VR和AR技术的发展,SLAM技术逐渐被人们熟知。SLAM技术是通过激光传感器感知周围的环境,并将不同时刻感知的环境进行匹配套合,从而反推本体在环境中的位置及运动轨迹。随着SLAM技术的设备及定位精度的提高,该技术逐渐在测绘行业中使用,迅速成为除RTK和全站仪采点成图之外的一种地形图成图手段。国外GeoSLAM等公司推出大量的产品,在该领域处于领先地位,为打破国外封锁,国内的飞马、数字绿土等公司也研发出相应的产品。该文主要通过具体的案例论述国产手持激光雷达扫描仪飞马SLAM 100在地形测绘中的运用,侧重于表述SLAM技术及SLAM100产品在运用中的优缺点。展开更多
Limited by the sampling capacity of the mobile devices, many real-time indoor location systems have such problems as low accuracy, large variance, and non-smooth movement of the estimated position. A new positioning a...Limited by the sampling capacity of the mobile devices, many real-time indoor location systems have such problems as low accuracy, large variance, and non-smooth movement of the estimated position. A new positioning algorithm and a new processing method for sampled data are proposed. Firstly, a positioning algorithm is designed based on the cluster-based nearest neighbour or probability. Secondly, a weighted average method with sliding window is used to process the sampled data as to overcome the mobile devices’ weak capability of signal sampling. Experimental results show that, for the general mobile devices, the accuracy of indoor position estimation increases from 56.5% to 76.6% for a 2-meter precision, and from 77.4% to 90.9% for a 3-meter precision. Therefore, the proposed methods can significantly and stably improve the positioning accuracy.展开更多
文摘SLAM的全称为Simultaneous Localization And Mapping即“同时定位与地图构建”。最近几年由于机器人、无人机、自动驾驶、AI、VR和AR技术的发展,SLAM技术逐渐被人们熟知。SLAM技术是通过激光传感器感知周围的环境,并将不同时刻感知的环境进行匹配套合,从而反推本体在环境中的位置及运动轨迹。随着SLAM技术的设备及定位精度的提高,该技术逐渐在测绘行业中使用,迅速成为除RTK和全站仪采点成图之外的一种地形图成图手段。国外GeoSLAM等公司推出大量的产品,在该领域处于领先地位,为打破国外封锁,国内的飞马、数字绿土等公司也研发出相应的产品。该文主要通过具体的案例论述国产手持激光雷达扫描仪飞马SLAM 100在地形测绘中的运用,侧重于表述SLAM技术及SLAM100产品在运用中的优缺点。
文摘Limited by the sampling capacity of the mobile devices, many real-time indoor location systems have such problems as low accuracy, large variance, and non-smooth movement of the estimated position. A new positioning algorithm and a new processing method for sampled data are proposed. Firstly, a positioning algorithm is designed based on the cluster-based nearest neighbour or probability. Secondly, a weighted average method with sliding window is used to process the sampled data as to overcome the mobile devices’ weak capability of signal sampling. Experimental results show that, for the general mobile devices, the accuracy of indoor position estimation increases from 56.5% to 76.6% for a 2-meter precision, and from 77.4% to 90.9% for a 3-meter precision. Therefore, the proposed methods can significantly and stably improve the positioning accuracy.
