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arxivcs.ROeess.SY2026-07-03

Derivations of Error-State Kalman Filter Kinematics for Globally Applicable Aided Inertial Navigation Systems

Antonia Hager, Torleiv H. Bryne

Global navigation systems require state estimation algorithms that handle Earth's curvature, Earth's rotation, and gravitational variations. These factors can typically be neglected in local navigation algorithms for robots, drones, etc. In classical error-state Kalman Filtering (ESKF) the error state dynamics are trajectory-dependent. Invariant ESKFs utilize Lie Group symmetries to represent the error, which can render error propagation trajectory-independent for group-affine systems. Choosing between a standard filter (where position and velocity errors are defined additively in the navigation frame), a left-invariant filter (where errors are represented in the body frame) and a right-invariant filter (where errors are represented in the navigation/world frame) depends on system dynamics and sensor configuration. This note presents the mathematical formulas for four classical and invariant ESKFs for globally applicable aided inertial navigation systems. It is intended to serve as a systematic reference for comparison and implementation.

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arxivcs.ROeess.SY2026-07-03

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arxivcs.ROeess.SY2026-07-04

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arxivcs.ROeess.SY2026-07-18

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arxivcs.ROeess.SY2026-07-12

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arxiveess.SYcs.RO2026-07-09

Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments

Siddhartha Upadhyay, Ratnangshu Das, Pushpak Jagtap

Safe navigation in dynamic environments is challenging when system dynamics are unknown and actuator inputs are limited. Existing methods either rely on accurate models, require online optimization, or do not explicitly account for input constraints. This paper presents a real-ti…

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