Developing Moving Horizon Estimation Based Ranging Measurement for Supporting Vision-Aided Inertial Navigation System
Bibliographic record
Abstract
The objective of this paper is to develop an advanced Vision-and-Ranging-aided Inertial Navigation System (VRINS), which combines a Vision-aided Inertial Navigation System (VINS) with Moving Horizon Estimation (MHE) based ranging measurement update. The traditional VINS estimate suffers the error accumulation from the camera observation, which makes the system diverge and fails to track the vehicle trajectory in long-term operation. Hence, a ranging sensor is integrated with VINS in the sequential-sensor-update structure, which allows the filter to operate for longer duration. The ranging measurement update is developed with the MHE, which directly incorporates the system constraints into the optimization process. The VINS is developed with Cubature Multi-State Constraint Kalman Filter (MSCKF), which has 30-dimension filter state, tight constraints of state transition and observability. Those elements need to be considered in the design of MHE optimization. The implementation of MHE is conducted with CASADI library. The proposed VRINS will be validated using KITTI dataset and compared against the VINS.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".