Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation.
Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation.
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DOI：
10.3390/s18020590
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发表时间：
2018-02-14
期刊：
影响因子：
--
通讯作者：
Mäkelä M
中科院分区：
文献类型：
--
作者：
Ruotsalainen L;Kirkko-Jaakkola M;Rantanen J;Mäkelä M
我们研究的长期目标是开发一种用于战术态势感知的无基础设施的同时定位和地图绘制(SLAM)和上下文识别的方法。定位将通过传播使用单目相机、脚载惯性测量单元(IMU)、声纳和气压计获得的运动测量来实现。由于战术应用对尺寸和重量的要求，将使用微电子机械(MEMS)传感器。然而，MEMS传感器会受到偏差和漂移误差的影响，这可能会大大降低位置精度。因此，复杂的误差建模和积分算法的实施是提供可行结果的关键。用于多传感器融合的算法传统上是卡尔曼滤波的不同版本。然而，卡尔曼滤波是基于状态传播和测量模型与加性高斯噪声是线性的假设。这两种假设都不适用于战术应用，尤其是对下马士兵或救援人员而言。因此，误差建模和高级融合算法的实施对于提供可行的结果是至关重要的。我们的方法是使用粒子滤波(PF)，这是一个复杂的选择，用于整合来自具有非高斯误差特征的行人运动的测量结果。本文讨论了惯性传感器和基于视觉的航向和平移测量的测量误差的统计建模，以便在粒子滤波实现中包含正确的误差概率密度函数(Pdf)。然后，用模型拟合法对测量误差的pdf进行了验证。在推导出的测量误差模型的基础上，发展了粒子滤波方法来融合所有这些信息，其中每个粒子的权重是根据得到的特定模型来计算的。通过两个实验对所开发方法的性能进行了测试，其中一个在大学校舍内进行，另一个在现实的战术条件下进行。结果表明，当测量误差被仔细建模并正确地将其包含在粒子滤波实现中时，水平定位得到了显着的改善。
The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF), which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university’s premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized.
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