Blinded by the Light: Exploiting the Deficiencies of a Laser Rangefinder for Rover Attitude Estimation
Bibliographic record
Abstract
This paper presents a method to exploit inherent deficiencies in the sensing technology of a SICK laser rangefinder to detect sun positions from 3D lidar scans. Given the common use of SICK lidars on mobile robots, this method enables sun sensing for some existing configurations without requiring additional hardware or configuration costs. Adding sun sensing to mobile rovers has clear advantages; for example, sun vectors can be combined with an inclinometer to calculate rover orientation in an absolute reference frame and used to improve pose estimates. The proposed sun sensing technique was verified using a SICK LMS-511 lidar mounted on a Schunk panning unit through two separate experiments. In the first experiment, the outputs of both our algorithm and a Sinclair Interplanetary SS411 digital sun sensor were compared to solar ephemeris data over an entire day. While the SS-411 has higher accuracy, the experiment showed that our lidar-based method has acceptable accuracy and a larger field of view (FOV) that covers the entire sky. In the second experiment, our sun sensing algorithm was used with an inclinometer to calculate the absolute orientation of the rover periodically during a traverse. This information was used with wheel odometry to estimate rover poses over the entire traverse, yielding more accurate results than wheel odometry alone. When including lidar-based sun measurements, the average estimate error over the entire traverse was only 8.4 metres, an 88% improvement over wheel odometry (70.4 metres). The resulting final position estimate error was 22.8 metres, or 2.76% of total distance travelled.
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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 | 0.000 | 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".