DC-VINS: Dynamic Camera Visual Inertial Navigation System with Online Calibration
Bibliographic record
Abstract
Visual-inertial (VI) sensor combinations are becoming ubiquitous in a variety of autonomous driving and aerial navigation applications due to their low cost, limited power consumption and complementary sensing capabilities. However, current VI sensor configurations assume a static rigid transformation between the camera and IMU, precluding manipulating the viewpoint of the camera independent of IMU movement which is important in situations with uneven feature distribution and for high-rate dynamic motions. Gimbal stabilized cameras, as seen on most commercially available drones, have seen limited use in SLAM due to the inability to resolve the time-varying extrinsic calibration between the IMU and camera needed in tight sensor fusion. In this paper, we present the online extrinsic calibration between a dynamic camera mounted to an actuated mechanism and an IMU mounted to the body of the vehicle integrated into a Visual Odometry pipeline. In addition, we provide a degeneracy analysis of the calibration parameters leading to a novel parameterization of the actuated mechanism used in the calibration. We build our calibration into the VINS-Fusion package and show that we are able to accurately recover the calibration parameters online while manipulating the viewpoint of the camera to feature rich areas thereby achieving an average RMSE error of 0.26m over an average trajectory length of 340m, 31.45% lower than a traditional visual inertial pipeline with a static camera.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".