一种用于视觉惯性里程计的改进型多状态约束卡尔曼滤波器
An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry
AI总结：
针对资源受限平台上MSCKF实时图像处理计算成本高的问题，本文通过特征边缘化与状态剪枝提出FMSCKF，并在数据集和真实实验中验证其速度提升约6倍、位置精度至少提高20%。
摘要：
快速位姿估计（PE）对于敏捷自主机器人成功执行任务至关重要。GPS和GNSS等全球定位系统通常与惯性导航系统（INS）融合用于位姿估计。然而，较低的更新频率以及缺乏适当信号，使其在室内和城市场景中的应用并不现实。另一方面，视觉惯性里程计（VIO）作为GPS拒止环境下GNSS/INS系统的实用替代方案正日益普及。在众多基于VIO的方法中，多状态约束卡尔曼滤波器（MSCKF）因其鲁棒性、速度和精度而受到更多关注。尽管如此，在资源受限的飞行器上实时实现MSCKF时，图像处理所带来的高计算成本仍是一项具有挑战性且持续开展的研究。本文提出了一种增强版MSCKF。为此，作者提出了不同的特征边缘化和状态剪枝策略，从而得到一种快得多的算法。所提算法在一个开源数据集和真实世界实验中均进行了测试以作验证。结果表明，与标准MSCKF算法相比，所提出的Fast-MSCKF（FMSCKF）速度约快六倍，并且最终位置估计精度至少提高20%。
英文摘要：
Fast pose estimation (PE) is of vital importance for successful mission performance of agile autonomous robots. Global Positioning Systems such as GPS and GNSS have been typically used in fusion with Inertial Navigation Systems (INS) for PE. However, the low update rate and lack of proper signals make their utility impractical for indoor and urban applications. On the other hand, Visual-Inertial Odometry (VIO) is gaining popularity as a practical alternative for GNSS/INS systems in GPS-denied environments. Among the many VIO-based methods, the Multi-State Constraint Kalman Filter (MSCKF) has received a greater attention due to its robustness, speed and accuracy. To this end, the high computational cost associated with image processing for real-time implementation of MSCKF on resource-constrained vehicles is still a challenging ongoing research. In this paper, an enhanced version of the MSCKF is proposed. To this aim, different feature marginalization and state pruning strategies are suggested that result in a much faster algorithm. The proposed algorithm is tested both on an open-source dataset and in real-world experiments for validation. It is demonstrated that the proposed Fast-MSCKF (FMSCKF) is about six times faster and at least 20% more accurate in final position estimation than the standard MSCKF algorithm.
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