Local Maps Are All You Need: A Review of Topometric Teach and Repeat Navigation
Why this work is in the frame
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Bibliographic record
Abstract
Teach and repeat (T&R) navigation has gained popularity over the past 15 years for its reliable path tracking in global navigation satellite system (GNSS)–denied environments. By using topometric navigation, it blends the scalability of graphs to connect distant places of interest, with metric estimates of a robot's state that allow control algorithms to correct path-tracking errors. Robots have been field-tested in off-road environments and found commercial applications in mining, cleaning, and agriculture. We present a summary of the different interpretations of T&R, introduce the relative strengths of various sensors in T&R, highlight methods that boost the reliability of T&R on robot hardware, and propose that the effectiveness of local maps in topometric navigation comes from a sensor-dependent bias–variance trade-off.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it