基于多传感器的室内建图导航系统的设计

Design of Indoor Mapping and Navigation System Based on Multi-sensor

针对基于单一传感器的移动机器人在建图与导航过程中定位精度低以及对环境描述能力有限的问题,开发了基于ROS(Robot Operating System)的多传感器感知的建图导航系统。首先搭建了具有全向性的四麦克纳姆轮移动底盘;其次分析了RTAB-MAP(Real-Time Appearance-Based Mapping)算法,并基于该算法对RGB-D相机、激光雷达和里程计信息进行数据融合,实现了室内环境的二维和三维地图的同时构建;然后提出了使用扩展卡尔曼滤波算法将编码器产生的里程计信息和IMU(Inertial Measurement Unit)数据进行融合,提高位姿的估计精度;最后根据融合后的数据对传统机器人导航框架进行了优化,完成了自主导航功能的设计。测试结果表明,该系统采用多传感器感知方案能够完成对室内场景的二维和三维地图的同时构建,提高了对环境的描述能力,通过扩展卡尔曼滤波融合后的数据使机器人定位精度有了明显提高,保证了导航的准确性。

Aiming at the problems of low positioning accuracy and limited ability to describe the environment in the process of map building and navigation of mobile robot based on single sensor,a multi-sensor sensing map building and navigation system based on robot operating system is developed.Firstly,an omnidirectional four-wheel Mecanum mobile chassis is built.Secondly,the RTAB-MAP algorithm is analyzed,and based on this algorithm,the RGB-D camera,lidar and odometer information are fused to realize the simultaneous construction of two-dimensional and three-dimensional maps of indoor environm ent.Thirdly,the extended Kalman filter algorithm is proposed to fuse the odometer information generated by the encoder with IMU data to improve the estimation accuracy of pose.Finally,the traditional robot navigation framework is optimized according to the fused data,the design of autonomous navigation function is completed.The test results show that the system adopts the multi-sensor sensing scheme,which can complete the construction of two-dimensional and three-dimensional maps of the indoor scene at the same time,and improve the ability to describe the environment.By using the fused data of extended Kalman filter,the positioning accuracy of the robot is significantly improved and the accuracy of navigation is ensured.