DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network
Bibliographic record
Abstract
Vision-based path following allows robots to autonomously repeat manually taught paths. Stereo Visual Teach and Repeat (VT&R) [1] accomplishes accurate and robust long-range path following in unstructured outdoor environments across changing lighting, weather, and seasons by relying on colour-constant imaging [2] and multi-experience localization [3]. We leverage multi-experience VT&R together with two datasets of outdoor driving on two separate paths spanning different times of day, weather, and seasons to teach a deep neural network to predict relative pose for visual odometry (VO) and for localization with respect to a path. In this paper we run experiments exclusively on datasets to study how the network generalizes across environmental conditions. Based on the results we believe that our system achieves relative pose estimates sufficiently accurate for in-the-loop path following and that it is able to localize radically different conditions against each other directly (i.e. winter to spring and day to night), a capability that our hand-engineered system does not have.
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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".