With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation ...With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.展开更多
传感器定位是实现自动驾驶的关键技术,但单一传感器定位系统由于传感器自身缺陷在面对复杂环境下存在定位精度不高,算法失灵等问题。相对而言,多传感器定位系统融合则可以弥补单一传感器缺陷,提高定位精度。为了提高系统定位精度,本文...传感器定位是实现自动驾驶的关键技术,但单一传感器定位系统由于传感器自身缺陷在面对复杂环境下存在定位精度不高,算法失灵等问题。相对而言,多传感器定位系统融合则可以弥补单一传感器缺陷,提高定位精度。为了提高系统定位精度,本文设计了一种基于自适应噪声的无迹卡尔曼滤波多定位系统融合算法。此算法选用CTRA(Constant Turn Rate and Acceleration)模型作为运动模型,根据时间序列法融合多种传感器构建观测模型,并在滤波算法中加入自适应噪声来适应复杂环境。实验表明,无自适应噪声的融合定位系统精度比单一激光和视觉定位系统分别提高6.8%和21.1%,而在有自适应噪声的情况下,进一步提高约1.5%。对比单一传感器定位系统,该算法有效提高了系统定位精度。展开更多
基金National Key Research and Development of China(No.2019YFB1600700)Sichuan Science and Technology Planning Project(No.2021YFSY0003)。
文摘With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.
文摘传感器定位是实现自动驾驶的关键技术,但单一传感器定位系统由于传感器自身缺陷在面对复杂环境下存在定位精度不高,算法失灵等问题。相对而言,多传感器定位系统融合则可以弥补单一传感器缺陷,提高定位精度。为了提高系统定位精度,本文设计了一种基于自适应噪声的无迹卡尔曼滤波多定位系统融合算法。此算法选用CTRA(Constant Turn Rate and Acceleration)模型作为运动模型,根据时间序列法融合多种传感器构建观测模型,并在滤波算法中加入自适应噪声来适应复杂环境。实验表明,无自适应噪声的融合定位系统精度比单一激光和视觉定位系统分别提高6.8%和21.1%,而在有自适应噪声的情况下,进一步提高约1.5%。对比单一传感器定位系统,该算法有效提高了系统定位精度。
