Integration of Topological Maps with GNSS and Onboard Sensors for Robust Land Vehicle Navigation
Bibliographic record
Abstract
Accurate automotive navigation systems are foundational to the efficacy and safety of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) technologies, necessitating continuous advancements to ensure reliability in the face of Global Navigation Satellite System (GNSS) outages and dynamic road conditions. Innovative techniques and algorithms have been developed and critically evaluated to enhance various facets of navigation performance, marking significant strides in vehicular navigation research. The exploration begins with advancements in odometry-based navigation independent of map data. Integration of Structure-from-Motion (SfM) with the reduced inertial sensor system (RISS) and GNSS substantially improves performance, particularly in managing complex road maneuvers. To better handle urban environments, a novel Semantic Segmentation-based Outlier Rejection (SS-OR) technique was developed to enhance the accuracy of visual odometry systems, presenting notable implications for autonomous navigation and mapping. Further investigation into automotive Wheel Speed Sensors (WSS) leads to the creation of a fusion engine that amalgamates WSS data with stereo visual odometry. This approach effectively minimizes forward velocity errors and biases, contributing significantly to the overall enhancement of navigation system capabilities. The discourse extends to map-aided navigation, introducing a Two-Stage Kinematic Update technique for topological map-matching (TMM) algorithms reliant on conventional GNSS/RISS integration. This innovation demonstrates considerable improvements in navigation accuracy during GNSS outages. Nonetheless, the persistence of challenges related to cumulative errors and drift underscores the necessity for expanded integration with additional perception systems. Concluding the research, the integration of topological maps with the proposed forward velocity fusion engine and the dynamic window approach (DWA) culminates in a comprehensive navigation solution. Although the outcomes are promising, opportunities for further enhancements remain, particularly in refining map details and addressing deviations from planned routes, paving the way for future explorations in the domain. The efficacy of the proposed methods is rigorously evaluated through a series of real-road experiments conducted in the Cities of Kingston and Toronto, designed to assess their viability and advantages comprehensively. The outcomes of these tests reveal a marked improvement in performance, showcasing the proposed methods' superiority in comparison to traditional navigation techniques.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".