A Geometric Approach For Pose and Velocity Estimation Using IMU and Inertial/Body-Frame Measurements
Bibliographic record
Abstract
This paper addresses accurate pose estimation (orientation and position) and linear velocity for a rigid body using a combination of generic inertial-frame and/or body-frame measurements along with an Inertial Measurement Unit (IMU). By embedding the original state space SO(3)× ℝ3× ℝ3into the higher-dimensional Lie group SE5(3), we reformulate the state dynamics and measurement models within a structured geometric framework. This embedding enables a natural decoupling of the geometric error dynamics, where the translational error dynamics exhibit a structure analogous to that of a continuous-time Kalman filter, allowing for a time-varying gain design based on the continuous Riccati equation. Under the assumption of uniform observability, we establish that the proposed observer on SE5(3) guarantees almost-global asymptotic stability. The effectiveness of the approach is demonstrated in simulations for a practical scenario of GPS-aided inertial navigation systems (INS). Overall, the proposed method significantly simplifies the design of nonlinear geometric observers for INS, offering a unified and robust approach to state estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".