A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments
Bibliographic record
Abstract
Grocery shoppers must negotiate cluttered, crowded, and complex store layouts containing a vast variety of products to make their intended purchases. This complexity may prevent even experienced shoppers from finding their grocery items, consuming a lot of their time and resulting in monetary loss for the store. To address these issues, we present a generic grocery robot architecture for the autonomous search and localization of products in crowded dynamic unknown grocery store environments using a unique context Simultaneous Localization and Mapping (contextSLAM) method. The contextSLAM method uniquely creates contextually rich maps through the online fusion of optical character recognition and occupancy grid information to locate products and aid in robot localization in an environment. The novelty of our robot architecture is in its ability to intelligently use geometric and contextual information within the context map to direct robot exploration in order to localize products in unknown environments in the presence of dynamic people. Extensive experiments were conducted with a mobile robot to validate the overall architecture and contextSLAM, including in a real grocery store. The results of the experiments showed that our architecture was capable of searching for and localizing all products in various grocery lists in different unknown environments.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".