Underground multi-sensor integrated navigation system
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摘要: 针对井下车辆高精度惯性导航成本较高、低成本MEMS惯性传感器漂移较大等问题，提出一种井下多传感器组合导航系统，该系统通过蓝牙测距信息、MEMS惯性传感器及车载里程计信息进行组合导航。利用卡尔曼滤波技术融合多传感器数据，结合蓝牙测距信息抑制MEMS惯性传感器漂移，提高车载惯性传感器在一段时间内的定位精度；通过MEMS惯性传感器预测车辆位置，有效滤除干扰标签的蓝牙信号，提高数据可靠性；融合车辆里程计数据后，定位结果更加稳定可靠。测试结果表明，在井下蓝牙标签间隔10 m布站情况下，每10 m定位误差在3.2 m以内，能够满足井下导航要求。Abstract: In view of problems of high cost of high-precision inertial navigation of underground vehicle and large drift of low-cost MEMS inertial sensor, an underground multi-sensor integrated navigation system was proposed, which uses bluetooth ranging information, MEMS inertial sensor and vehicle odometer information for integrated navigation. The system uses Kalman filter technology to fuse multi-sensor data, combines Bluetooth ranging information to suppress MEMS inertial sensor drift, and improve positioning accuracy of inertial sensor in a period of time; Vehicle position is predicted by MEMS inertial sensor to effectively filter out Bluetooth signal of interference tags, so as to improves data reliability; Integration of odometer data can make the positioning result more stable and reliable. The test results show that the positioning error of every 10 m is less than 3.2 m under the condition that the underground Bluetooth tags are distributed 10 m apart, which can meet requirements of underground navigation.
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期刊类型引用(2)
1. 刘仕杰，邹渊，张旭东. 基于局部几何-拓扑地图的地下矿自动驾驶定位导航方法. 工矿自动化. 2023(08): 70-80 . 
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2. 王伟. 基于卡尔曼滤波和加权LM法的井下精确定位算法. 工矿自动化. 2019(11): 5-9 . 本站查看
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