Landmark Position and Full Pose Estimation of a Rigid Body Using Bearing Measurements

This paper studies the method of estimating the landmark positions as well as the full pose of a rigid body, including the velocity, location, and attitude. First, we propose a linear time-varying (LTV) observer to estimate the states in body frame (landmark locations, gravity, and inertial vector) using an inertial measurement unit (IMU), a monocular camera, and a magnetometer. Thereafter, the landmark position and the full pose are reconstructed in an attitude estimation module and a location reconstruction module. Our observer is proved to be uniformly observable (UO) under a persistency of excitation (PE) condition and uniformly ultimately bounded (UUB) under bounded perturbation. Sufficient simulations are completed to indicate the capability of our proposed scheme.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icaisisas64483.2025.11051680
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00

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article

Landmark Position and Full Pose Estimation of a Rigid Body Using Bearing Measurements

Shanying Zhu, Ziwen Yang, Yifan Ji
Robotics and Sensor-Based Localization
article

Landmark Position and Full Pose Estimation of a Rigid Body Using Bearing Measurements

Shanying Zhu, Ziwen Yang, Yifan Ji
article en

Abstract

This paper studies the method of estimating the landmark positions as well as the full pose of a rigid body, including the velocity, location, and attitude. First, we propose a linear time-varying (LTV) observer to estimate the states in body frame (landmark locations, gravity, and inertial vector) using an inertial measurement unit (IMU), a monocular camera, and a magnetometer. Thereafter, the landmark position and the full pose are reconstructed in an attitude estimation module and a location reconstruction module. Our observer is proved to be uniformly observable (UO) under a persistency of excitation (PE) condition and uniformly ultimately bounded (UUB) under bounded perturbation. Sufficient simulations are completed to indicate the capability of our proposed scheme.

Shanghai Jiao Tong University (CN), Southeast University (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 78%
Robotics and Sensor-Based Localization
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