Factor graph localization for mobile robots using Google Indoor Street View and CNN-based place recognition
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
This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN)-based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4% for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it