基于改进图优化的惯性/卫星/视觉组合导航方法

Inertial/satellite/vision integrated navigation method based on improved graph optimization

惯性/卫星/视觉组合导航在城市复杂环境下可以获得较高精度的连续定位信息,因此应用越来越广泛。但外界环境变化引起的传感器误差会导致组合导航精度下降,甚至造成组合导航结果发散。针对低成本惯性/卫星/视觉组合导航过程中因环境因素产生的传感器误差问题,提出基于改进图优化算法的惯性/卫星/视觉组合导航算法。在传统图优化算法的基础上,在代价函数中引入鲁棒核函数对环境引起的传感器误差进行过滤,并且在惯性测量单元(inertial measurement unit,IMU)预积分中加入地球自转加速度补偿来提高预积分精度,进而提高组合导航精度。最后基于kaist城市行驶数据集设计了对比实验,验证结果表明,提出的改进图优化方法可以有效过滤因外界环境因素产生的传感器误差,相比于传统的图优化方法导航精度提升了7%。

Inertial/satellite/visual integrated navigation can obtain highly accurate continuous positioning information in complex urban environments,and therefore has become increasingly widely used.However,sensor gross errors caused by changes in the external environment can lead to a decrease in the accuracy of integrated navigation,and even cause divergence in the results of integrated navigation.In response to the problem of sensor gross errors caused by environmental factors in the low-cost inertial/satellite/visual integrated navigation process,an inertial/satellite/visual integrated navigation algorithm based on an improved graph optimization algorithm was proposed.A robust kernel function was introduced to the cost function based on the traditional graph optimization algorithm to filter the sensor gross errors caused by environmental factors,and earth rotation acceleration compensation to the IMU(inertial measurement unit)pre-integration was added to improve the accuracy of pre-integration,thus,the accuracy of integrated navigation was improved.Finally,a comparative experiment was designed based on the kaist urban driving dataset,and the results show that the improved graph optimization method proposed can effectively filter sensor gross errors caused by external environmental factors,and the navigation accuracy improves by 7%compared to the traditional graph optimization method.