The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter
Bibliographic record
Abstract
Accurate and consistent ego motion estimation is a critical component of autonomous navigation. For this task, the combination of visual and inertial sensors is an inexpensive, compact, and complementary hardware suite that can be used on many types of vehicles. In this work, we compare two modern approaches to ego motion estimation: the Multi-State Constraint Kalman Filter (MSCKF) and the Sliding Window Filter (SWF). Both filters use an Inertial Measurement Unit (IMU) to estimate the motion of a vehicle and then correct this estimate with observations of salient features from a monocular camera. While the SWF estimates feature positions as part of the filter state itself, the MSCKF optimizes feature positions in a separate procedure without including them in the filter state. We present experimental characterizations and comparisons of the MSCKF and SWF on data from a moving hand-held sensor rig, as well as several traverses from the KITTI dataset. In particular, we compare the accuracy and consistency of the two filters, and analyze the effect of feature track length and feature density on the performance of each filter. In general, our results show the SWF to be more accurate and less sensitive to tuning parameters than the MSCKF. However, the MSCKF is computationally cheaper, has good consistency properties, and improves in accuracy as more features are tracked.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".