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Main Authors: Wang, Maosong, Cui, Jiarui, Wu, Wenqi, Li, Peiqi, Pan, Xianfei
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2601.16078
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author Wang, Maosong
Cui, Jiarui
Wu, Wenqi
Li, Peiqi
Pan, Xianfei
author_facet Wang, Maosong
Cui, Jiarui
Wu, Wenqi
Li, Peiqi
Pan, Xianfei
contents One of the core advantages of SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. In the previous paper, the theoretical analysis of autonomy property of navigation model in inertial, earth and world frames was given. A construction method for SE2(3) group navigation model is proposed to improve the non-inertial navigation model toward full autonomy. This paper serves as a counterpart to previous paper and conducts the real-world strapdown inertial navigation system (SINS)/odometer(ODO) experiments as well as Monte-Carlo simulations to demonstrate the performance of improved SE2(3) group based high-precision navigation models.
format Preprint
id arxiv_https___arxiv_org_abs_2601_16078
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Improve the autonomy of the SE2(3) group based Extended Kalman Filter for Integrated Navigation: Application
Wang, Maosong
Cui, Jiarui
Wu, Wenqi
Li, Peiqi
Pan, Xianfei
Robotics
Systems and Control
One of the core advantages of SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. In the previous paper, the theoretical analysis of autonomy property of navigation model in inertial, earth and world frames was given. A construction method for SE2(3) group navigation model is proposed to improve the non-inertial navigation model toward full autonomy. This paper serves as a counterpart to previous paper and conducts the real-world strapdown inertial navigation system (SINS)/odometer(ODO) experiments as well as Monte-Carlo simulations to demonstrate the performance of improved SE2(3) group based high-precision navigation models.
title Improve the autonomy of the SE2(3) group based Extended Kalman Filter for Integrated Navigation: Application
topic Robotics
Systems and Control
url https://arxiv.org/abs/2601.16078