Appearance‐based landmark selection for visual localization
Bibliographic record
Abstract
Abstract Visual localization in outdoor environments is subject to varying appearance conditions rendering it difficult to match current camera images against a previously recorded map. Although it is possible to extend the respective maps to allow precise localization across a wide range of differing appearance conditions, these maps quickly grow in size and become impractical to handle on a mobile robotic platform. To address this problem, we present a landmark selection algorithm that exploits appearance co‐observability for efficient visual localization in outdoor environments. Based on the appearance condition inferred from recently observed landmarks, a small fraction of landmarks useful under the current appearance condition is selected and used for localization. This allows to greatly reduce the bandwidth consumption between the mobile platform and a map backend in a shared‐map scenario, and significantly lowers the demands on the computational resources on said mobile platform. We derive a landmark ranking function that exhibits high performance under vastly changing appearance conditions and is agnostic to the distribution of landmarks across the different map sessions. Furthermore, we relate and compare our proposed appearance‐based landmark ranking function to popular ranking schemes from information retrieval, and validate our results on the challenging University of Michigan North Campus long‐term vision and LIDAR data sets ( NCLT ), including an evaluation of the localization accuracy using ground‐truth poses. In addition to that, we investigate the computational and bandwidth resource demands. Our results show that by selecting 20–30% of landmarks using our proposed approach, a similar localization performance as the baseline strategy using all landmarks is achieved.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".