Indoor Temporally Constrained Instantaneous Ego-Motion Estimation Using 4-D Doppler Radar
Bibliographic record
Abstract
Indoor ego-motion estimation using millimeter-wave Doppler sensors is challenging due to high levels of outliers, primarily caused by multipath reflections. A standard approach to mitigate these outliers is random sample consensus (RANSAC), where the ego-motion model is derived from data collected at a single time step, overlooking the continuity between successive measurements. In this article, we demonstrate that leveraging temporal relationships across multiple time steps can improve ego-motion estimation accuracy in indoor environments. We introduce two novel RANSAC-based methods that incorporate a weighted sliding window to enhance ego-motion estimation: temporal sample consensus (TEMPSAC) and temporally weighted least squares (TWLSQ). In TEMPSAC, samples are selected with a probability-weighted by their temporal proximity, and the velocity model is generated using least-squares regression (LSQ). In TWLSQ, samples are uniformly selected, but the velocity model is parameterized with a temporally weighted LSQ. Both methods calculate the platform’s motion by prioritizing temporally consistent inliers. Experimental validation of 18 indoor trajectories shows an average position accuracy improvement of 27% compared to previous RANSAC-based ego-motion implementations. The results demonstrate the effectiveness of incorporating temporal information into mmWave-based ego-motion 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".