Spatial Calibration of IMU/Radar Sensors Using Single Target and Differential IMU Measurements
Bibliographic record
Abstract
In this work, we address the problem of spatial calibration between a Frequency Modulated Continuous Wave (FMCW) radar and an inertial measurement unit (IMU) sensor. Radar-IMU calibration is particularly valuable as a GPS-independent navigation solution especially in challenging weather conditions where other sensors may fail. Our approach employs a non-linear least squares formulation using the LevenbergMarquardt (LM) optimization algorithm to estimate the extrinsic parameters between the two sensors using a single target. We overcome the challenge of IMU's drift accumulation by using the relative poses of the sensor, and the calibration algorithm is extensively tested under varying poses of the sensor system and noise levels. We use a RANSAC-based plane-fitting algorithm for robust target detection. Simulation results demonstrate the effectiveness of the proposed algorithm, showing that using a single target reflector with approximately 10-20 sensor poses achieves robust alignment, with rotation errors under$\pm 1$degree and translation errors under$\pm 1 ~\text{cm}$. The proposed technique, therefore, provides an efficient and practical method for calibrating the radar-IMU system.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".