The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter
The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter
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DOI：
10.1109/crv.2015.11
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发表时间：
2015-06
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影响因子：
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通讯作者：
Lee Clement;Valentin Peretroukhin;Jacob Lambert;Jonathan Kelly
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作者：
Lee Clement;Valentin Peretroukhin;Jacob Lambert;Jonathan Kelly
准确一致的自我运动估计是自主导航的关键组成部分。对于这项任务，视觉和惯性传感器的组合是一种廉价，紧凑和互补的硬件套件，可用于许多类型的车辆。在这项工作中，我们比较了两种现代的自我运动估计方法：多状态约束卡尔曼滤波器（MSCKF）和滑动窗口滤波器（SINS）。这两种滤波器都使用惯性测量单元（IMU）来估计车辆的运动，然后通过单目相机观察到的显著特征来校正该估计。虽然滤波器估计特征位置作为滤波器状态本身的一部分，但MSCKF在单独的过程中优化特征位置，而不将它们包括在滤波器状态中。我们提出了实验表征和比较的MSCKF和muscron数据从移动的手持传感器装置，以及几个遍历KITTI数据集。特别地，我们比较了两种滤波器的准确性和一致性，并分析了特征轨道长度和特征密度对每个滤波器性能的影响。在一般情况下，我们的研究结果表明，更准确，更不敏感的调谐参数比MSCKF。然而，MSCKF在计算上更便宜，具有良好的一致性属性，并且随着跟踪更多的特征而提高精度。
Accurate and consistent ego motion estimation is a critical component of autonomous navigation. For this task, the combination of visual and inertial sensors is an inexpensive, compact, and complementary hardware suite that can be used on many types of vehicles. In this work, we compare two modern approaches to ego motion estimation: the Multi-State Constraint Kalman Filter (MSCKF) and the Sliding Window Filter (SWF). Both filters use an Inertial Measurement Unit (IMU) to estimate the motion of a vehicle and then correct this estimate with observations of salient features from a monocular camera. While the SWF estimates feature positions as part of the filter state itself, the MSCKF optimizes feature positions in a separate procedure without including them in the filter state. We present experimental characterizations and comparisons of the MSCKF and SWF on data from a moving hand-held sensor rig, as well as several traverses from the KITTI dataset. In particular, we compare the accuracy and consistency of the two filters, and analyze the effect of feature track length and feature density on the performance of each filter. In general, our results show the SWF to be more accurate and less sensitive to tuning parameters than the MSCKF. However, the MSCKF is computationally cheaper, has good consistency properties, and improves in accuracy as more features are tracked.