Towards seamless localization in challenging environments via high-definition maps and multi-sensor fusions
Bibliographic record
Abstract
In the realm of autonomous vehicles, shifting scenarios can lead to localization failures, due to challenges like GPS signal loss and structural degradation. To address these issues, our paper introduces a multi-sensor and high-definition (HD) map-based framework for resilient vehicle localization in challenging environments. The framework integrates GPS, LiDAR, and ultra-wideband (UWB) to provide GPS localization results, UWB range measurements, LiDAR odometry results, and range measurements from plane detection and HD maps. These observations are then transformed into diverse constraints of varying scales, which are further represented as factors. Enhanced by a self-calibration module, these factors are seamlessly fused within a factor graph framework to achieve robust vehicle localization. Specifically, in environments where GPS is available and structured, the proposed method fuses results from GPS positioning and LiDAR simultaneous localization and mapping (SLAM). In GPS-unavailable and structured scenarios, fusion involves LiDAR SLAM results and UWB ranging information. For GPS-unavailable and degenerated-structured environments, the fusion incorporates range measurements from LiDAR plane detection and UWB anchors. We validate the effectiveness of the proposed method through practical applications in diverse environments covering a total driving distance of approximately 20.54 km, including underground parking, tunnels, roads under urban viaducts, and typical urban roads.
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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".