Field Testing and Evaluation of Single-Receiver GPS Odometry for Use in Robotic Navigation
Bibliographic record
Abstract
Mobile robots rely on odometry to navigate in areas where localization fails. Visual odometry (VO), for instance, is a common solution for obtaining robust and consistent relative motion estimates of the vehicle frame. In contrast, Global Positioning System (GPS) measurements are typically used for absolute positioning and localization. However, when the constraint on absolute accuracy is relaxed, accurate relative position estimates can be found with one single-frequency GPS receiver by using time-differenced carrier phase (TDCP) measurements. In this paper, we implement and field test a single-receiver GPS odometry algorithm based on the existing theory of TDCP. We tailor our method for use on an unmanned ground vehicle (UGV) by incorporating proven robotics tools such as a vehicle motion model and robust cost functions. In the first half of our experiments, we evaluate our odometry on its own via a comparison with VO on the same test trajectories. After 4.3 km of testing, the results show our GPS odometry method has a 79% lower drift rate than a proven stereo VO method while maintaining a smooth error signal despite varying satellite availability. GPS odometry can also make robots more robust to catastrophic failures of their primary sensor when added to existing navigation pipelines. To prove this, we integrate our GPS odometry solution into Visual Teach and Repeat (VT&R), an established visual, path-following navigation framework. We perform further testing to show it can maintain accurate path following and prevent failures in challenging conditions including full camera dropouts. Code is available at https://github.com/utiasASRL/cpo.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".