The Impact of Vehicle Maneuvers on the Attitude Estimation of GNSS / INS for Mobile Mapping
Bibliographic record
Abstract
Abstract Integrated Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS) are the core of georeferencing Mobile Mapping Systems (MMS) data. Divergence of attitude errors is a dominant issue when an INS has to work as a stand-alone system for extended periods. This issue can be mitigated by taking specific vehicle maneuvers to make attitude errors observable. Since MMS applications are time consuming and costly, it is preferable to design the trajectory and motion of the mapping vehicles in advance, to guarantee the accuracy of the attitude estimation and minimize the cost. This article investigates the estimation accuracy of attitude under different vehicle maneuvers theoretically through the observability analysis method. Both theoretical analysis and tests show that the attitude estimation is significantly related with the type of vehicle maneuvers and motion parameters such as velocity, acceleration, and angular velocity. The motion with varying angular velocities is the most efficient motion to enhance the estimation of all attitude angles; the motion with varying accelerations can improve the yaw and pitch but has no effect on enhancing the roll. The uniform circular motion can improve the roll and pitch but has slight or no impact on enhancing the yaw (depending on the forward accelerometer error, the forward velocity, and the vertical angular velocity); the linear motion with a constant acceleration can improve the yaw (depending on the cross-track accelerometer error and the forward acceleration) and weakly improve the pitch but cannot improve the roll. The physical interpretations of these properties are also provided. The “S”-shaped motion with varying angular velocities is suggested for efficient attitude estimation; however, the circle, or “8”-shaped motion with uniform angular velocity, is not efficient for MMS applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".