文献类型：期刊文献

英文题名：Efficient Invariant Kalman Filter for Inertial-based Odometry with Large-sample Environmental Measurements

作者：Li, Xinghan[1,2]; Li, Haoying[2]; Zeng, Guangyang[2]; Zeng, Qingcheng[2,3]; Ren, Xiaoqiang[4]; Yang, Chao[5]; Wu, Junfeng[2]

机构：[1] College of Control Science and Engineering, Zhejiang University, Hangzhou, China; [2] School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; [3] The Hong Kong University of Science and Technology [Guangzhou], Guangzhou, China; [4] School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China; [5] Department of Automation, East China University of Science and Technology, Shanghai, China

年份：2024

外文期刊名：arXiv

收录：EI(收录号：20240075044)

语种：英文

外文关键词：Extended Kalman filters - Lie groups - Machine learning - Newton-Raphson method - Numerical methods

摘要：A filter for inertial-based odometry is a recursive method used to estimate the pose from measurements of ego-motion and relative pose. Currently, there is no known filter that guarantees the computation of a globally optimal solution for the non-linear measurement model. In this paper, we demonstrate that an innovative filter, with the state being SE2(3) and the √n-consistent pose as the initialization, efficiently achieves asymptotic optimality in terms of minimum mean square error. This approach is tailored for real-time SLAM and inertial-based odometry applications. Our first contribution is that we propose an iterative filtering method based on the Gauss-Newton method on Lie groups which is numerically to solve the estimation of states from a priori and non-linear measurements. The filtering stands out due to its iterative mechanism and adaptive initialization. Second, when dealing with environmental measurements of the surroundings, we utilize a √n-consistent pose as the initial value for the update step in a single iteration. The solution is closed in form and has computational complexity O(n). Third, we theoretically show that the approach can achieve asymptotic optimality in the sense of minimum mean square error from the a priori and virtual relative pose measurements (see Problem 2.2). Finally, to validate our method, we carry out extensive numerical and experimental evaluations. Our results consistently demonstrate that our approach outperforms other state-of-the-art filter-based methods, including the iterated extended Kalman filter and the invariant extended Kalman filter, in terms of accuracy and running time. Copyright ? 2024, The Authors. All rights reserved.