GROUNDED: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mapping and localization using surface features is prone to failure due to environment changes such as inclement weather. Recently, Localizing Ground Penetrating Radar (LGPR) has been proposed as an alternative means of localizing using underground features that are stable over time and less affected by surface conditions. However, due to the lack of commercially available LGPR sensors, the wider research community has been largely unable to replicate this work or build new and innovative solutions. We present GROUNDED, an open dataset of LGPR scans collected in a variety of environments and weather conditions. By labeling these data with ground truth localization from an RTK-GPS/Inertial Navigation System, and carefully calibrating and time-synchronizing the radar scans with ground truth positions, camera imagery, and lidar data, we enable researchers to build novel localization solutions that are resilient to changing surface conditions. We include 108 individual runs totaling 450 km of driving with LGPR, GPS, odometry, camera, and lidar measurements. We also present two new evaluation benchmarks for 1) localizing in weather and 2) multi-lane localization, to enable comparisons of future work supported by the dataset. Additionally, we present a first application of the new dataset in the form of LGPRNet: an inception-based CNN architecture for learning localization that is resilient to changing weather conditions. The dataset can be accessed at http://lgprdata.com .
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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 | 0.006 | 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 | 0.001 | 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 it