RADAR/INS INTEGRATION FOR POSE ESTIMATION IN GNSS-DENIED ENVIRONMENTS
Bibliographic record
Abstract
Abstract. This paper proposes a novel algorithm to use Radar in ego-motion estimation for autonomous navigation applications. This method is based on the analysis of Radar data to remove noise, ghost points, and outliers and keep the accurate features. From the detected features and the knowledge of Radar data rate and the vehicle's average speed, the change in range and azimuth between any two points can be constrained to find the corresponding points. With the help of the corresponding points, the vehicle's ego-motion can be estimated. Then, Radar is integrated with an Inertial Navigation System (INS) and odometer through an extended Kalman filter (EKF) to smooth the Radar solution and aid INS to overcome its large drifts in GNSS denied environments. Two real data were collected from frequency modulated continuous wave (FMCW) Radar sensors and Inertial Measurement Unit (IMU) in suburban areas near the University of Calgary, Canada. The proposed algorithm was tested by introducing simulated GNSS signal outages with different durations. The Root Mean Square Error (RMSE) for the horizontal position was improved by an average of 30.44% and 4.76% if it was compared with RMSE from odometer/INS solution with a percentage error less than 1% of the traveled distance which was 1.59 km and 2 km for the two datasets, respectively.
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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 | 0.001 |
| 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".