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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".