mlsanityData to 14 Oct 2026

Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

arXiv 2501.06878 ↗First posted 12 Jan 2025cs.CVcited by 6

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Abstract (arXiv)

Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains underexplored. We present the first approach to integrate uncertainty awareness into online extrinsic calibration, combining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a guaranteed level of coverage. Our method proposes a framework to enhance existing calibration models with uncertainty quantification, compatible with various network architectures. Validated on KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR) datasets, we demonstrate effectiveness across different visual sensor types, measuring performance with adapted metrics to evaluate the efficiency and reliability of the intervals. By providing calibration parameters with quantifiable confidence measures, we offer insights into the reliability of calibration estimates, which can greatly improve the robustness of sensor fusion in dynamic environments and usefully serve the Computer Vision community.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 12 Jan 2025.

Builds on

23 of 45 references.

References · 23

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  5. 2016Distribution-Free Predictive Inference For Regressioncited by 668
  6. 2019Conformalized Quantile Regressioncited by 629
  7. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  8. 2021DSEC: A Stereo Event Camera Dataset for Driving Scenarioscited by 303
  9. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 283
  10. 2017Concrete Dropoutcited by 227
  11. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  12. 2018Bounding Box Regression with Uncertainty for Accurate Object Detectioncited by 143
11 more
  1. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  2. 2018Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detectioncited by 82
  3. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  4. 2017RegNet: Multimodal Sensor Registration Using Deep Neural Networkscited by 69
  5. 2021Valid prediction intervals for regression problemscited by 29
  6. 2020Notes on the Behavior of MC Dropoutcited by 23
  7. 2022DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flowcited by 16
  8. 2021How to Evaluate Uncertainty Estimates in Machine Learning for Regression?cited by 15
  9. 2023Fortuna: A Library for Uncertainty Quantification in Deep Learningcited by 14
  10. 2024MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibrationcited by 7
  11. 2023UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Epistemic Uncertainty in Conformal Scores: A Unified Approachcited by 9
  2. 2025Performance of Conformal Prediction in Capturing Aleatoric Uncertaintycited by 1
  3. 2025Automated C-Arm Positioning via Conformal Landmark Localizationcited by 1
  4. 2026Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service marketscited by 0
  5. 2026calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environmentscited by 0
  6. 2026CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regressioncited by 0