Towards appearance-based methods for lidar sensors
Why this work is in the frame
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Bibliographic record
Abstract
Cameras have emerged as the dominant sensor modality for localization and mapping in three-dimensional, unstructured terrain, largely due to the success of sparse, appearance-based techniques, such as visual odometry. However, the Achilles' heel for all camera-based systems is their dependence on consistent ambient lighting, which poses a serious problem in outdoor environments that lack adequate or consistent light, such as the Moon. Actively illuminated sensors on the other hand, such as a light detection and ranging (lidar) device, use their own light source to illuminate the scene, making them a favourable alternative in light-denied environments. The purpose of this paper is to demonstrate that the largely successful appearance-based methods traditionally used with cameras can be applied to laser-based sensors, such as a lidar. We present two experiments that are vital to understanding and enabling appearance-based methods for lidar sensors. In the first experiment, we explore the stability of a representative keypoint detection and description algorithm on both camera images and lidar intensity images collected over a 24 hour period. In the second experiment, we validate our approach by implementing visual odometry based on sparse bundle adjustment on a sequence of lidar intensity images.
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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 it