State Estimation Using Single Body-Frame Bearing Measurements

Core Concepts

Proposing a Riccati observer-based estimator for state estimation using IMU and single bearing measurements.

Abstract

The paper addresses the simultaneous estimation of position, linear velocity, and orientation of a rigid body using a Riccati observer-based estimator. It discusses the challenges in accurate pose estimation for autonomous robotic platforms and the limitations of traditional INS systems. The proposed observer guarantees global exponential convergence under specific conditions on the vehicle's motion. Various algorithms integrating bearing measurements are explored, emphasizing the advantages of nonlinear deterministic observers over stochastic filters. The content delves into vision-aided INS, underwater applications, and reduced-order versions of the observer for attitude estimation. Detailed mathematical formulations, observability analysis, simulation results, and future work are presented.

Structure:

• Introduction to State Estimation Challenges
• Importance of Inertial Navigation Systems (INS)
• Integration of Bearing Measurements in Estimation Algorithms
• Proposed Riccati Observer-Based Estimator
• Observability Analysis and Main Results
• Simulation Results and Performance Evaluation
• Conclusion and Future Directions

Stats

"Simulation results are presented to show the effectiveness of the proposed approach."
"The gravity vector gI is set to [0 0 9.81]⊤."
"The constant vector mI is set to [1/√2 0 1/√2]⊤."

Quotes

"The proposed observer guarantees global exponential convergence under some persistency of excitation condition on the vehicle’s motion."
"Most ad-hoc methods assume RaB ≈ −gI to remove coupling between translational and rotational dynamics."
"The proposed observer provides an estimation of pB, vB, gB, and mB using available measurements."

Key Insights Distilled From

State Estimation Using Single Body-Frame Bearing Measurements

by Sifeddine Be... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12633.pdf

Deeper Inquiries

How can biased IMU measurements be integrated into the proposed observer while maintaining global convergence?

Incorporating biased IMU measurements into the proposed observer while ensuring global convergence involves adjusting the state-space model to account for these biases. Biases in IMU measurements can significantly impact the accuracy of the estimation process, leading to errors in position, velocity, and orientation estimates. To address this issue:

Model Augmentation: The state-space model should be extended to include bias terms for accelerometer and gyroscope readings. By treating these biases as additional states in the system, their evolution equations can be incorporated into the observer dynamics.

Bias Estimation: The observer needs to estimate not only the vehicle's position, velocity, and attitude but also the biases present in IMU measurements. This requires designing a separate filter or mechanism within the observer structure to track and compensate for these biases over time.

Observer Design: The Riccati-based observer must be modified to handle biased measurements effectively. The gain matrix calculation should consider bias terms when updating estimates based on sensor readings.

Noise Handling: Biases introduce noise that affects measurement accuracy. Proper handling of this noise through covariance matrices adjustment is crucial for robust estimation performance.

Validation Mechanisms: Implementing validation mechanisms such as consistency checks between different sensors or redundancy checks can help detect and mitigate issues arising from biased IMU data.

By integrating these strategies into the proposed observer design, it is possible to accommodate biased IMU measurements while maintaining global convergence and improving overall estimation accuracy.

What are the implications of assuming RaB ≈ −gI in practice for vehicles with non-accelerated motion?

Assuming RaB ≈ −gI implies considering that any apparent acceleration measured by an Inertial Measurement Unit (IMU) is primarily due to gravity acting on a stationary or non-accelerated vehicle rather than external forces or accelerations.
The implications of this assumption include:

Simplified Dynamics: By equating apparent acceleration with -gI (gravity vector), it simplifies the dynamics model used for navigation systems since gravitational effects are well-defined constants.

Decoupling Translational Motion: It decouples translational motion from rotational dynamics captured by angular velocities, making it easier to analyze linear accelerations independently without being influenced by other factors like external disturbances.

3 .Error Propagation: However, if there are actual accelerations present due to movements or external forces acting on a vehicle (especially during maneuvers), inaccuracies may arise as they would not be accounted for under this assumption.
4 .Impact on Navigation Accuracy: In scenarios where vehicles experience significant accelerations or dynamic motions (e.g., UAVs performing agile maneuvers), relying solely on RaB ≈ -gI assumption may lead to degraded navigation accuracy due to unaccounted inertial forces affecting sensor readings.

How can reduced-order or decoupled observers enhance robustness in UAV flights by estimating roll/pitch independently from yaw?

Reduced-order or decoupled observers play a vital role in enhancing robustness during Unmanned Aerial Vehicle (UAV) flights by enabling independent estimation of roll/pitch angles from yaw information:
1 .Reduced Complexity: These observers simplify computations by focusing on specific aspects of pose estimation rather than dealing with all degrees of freedom simultaneously.
2 .Improved Stability: By isolating roll/pitch estimations from yaw calculations using reduced-order techniques like singular value decomposition or projection methods allows more stable tracking especially when dealing with noisy sensor data
3 .Fault Tolerance: Decoupling pitch/roll estimations enhances fault tolerance since errors affecting one parameter do not directly propagate across all axes potentially reducing catastrophic failures
4 .Redundancy Checks:
Independent estimators provide an additional layer of redundancy allowing comparison between estimated values aiding error detection
5 .Enhanced Control Strategies:
Accurate individual angle estimates enable precise control strategies tailored towards each degree-of-freedom optimizing flight performance
6 ,Adaptive Filtering Techniques:
Reduced-order observers facilitate integration with adaptive filtering techniques ensuring real-time adjustments based on changing flight conditions
By leveraging reduced-order or decoupled observers specifically designed for UAV applications where distinct control over roll/pitch angles is critical; stability reliability & precision during flight operations are significantly improved