Multi-Sensor Based Land Vehicles’ Positioning in Challenging GNSS Environments
Bibliographic record
Abstract
The car industry has a growing demand for reliable, continuous, and accurate positioning information for various applications, including routing to a specific destination, asset tracking, and, eventually future self-driving. Global navigation satellite system (GNSS) receivers have been widely used for this purpose. However, adequate GNSS positioning accuracy cannot be guaranteed in all environments due to possible satellite signal blockage, poor satellite geometry, and multipath in urban environments and downtown cores. The technological advances and low cost of micro-electro-mechanical system (MEMS) – based inertial sensors (accelerometers and gyroscopes) enabled their use inside land vehicles for various reasons, including the integration with GNSS receivers to provide positioning information that can bridge GNSS outages in challenging GNSS environments. An optimal estimation technique, such as the Kalman filter, is used to integrate the positioning solution from both the GNSS receiver and the inertial sensors. However, in dense urban areas and downtown cores where GNSS receivers may incur prolonged outages, the integrated positioning solution may become prone to rapid drift resulting in substantial position errors. Therefore, it is becoming necessary to include other sensors and systems that can be available in future land vehicles to integrate with both the GNSS receivers and inertial sensors to enhance the positioning performance in such challenging environments. The aim of this research is to design and examine the performance of a multi-sensor integrated positioning system that fuses the GNSS receiver data with not only inertial sensors but also with the three-dimensional point cloud of onboard light detection and ranging (LiDAR) system. In this thesis, a comprehensive LiDAR processing and odometry method is developed to provide a continuous and accurate positioning solution, even in challenging GNSS environments. A multi-sensor fusion based on extended Kalman filtering is also developed to integrate the LiDAR positioning information with both GNSS and inertial sensors and utilize the LiDAR updates to limit the drift in the positioning solution, even in challenging or completely denied GNSS environment. The performance of the proposed multi-sensor positioning solution is examined using several road test trajectories in both Kingston and Toronto downtown areas involving different vehicle dynamics and driving scenarios. This thesis discusses the merits and limitations of the proposed method and gives recommendations for future research.
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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 | 0.000 |
| 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 | 0.000 |
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 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".