Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones

• URL: http://arxiv.org/abs/2608.26548v1
• Date: Thu, 27 Aug 2026 02:39:40 GMT
• Title: Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones
• Abstract summary: There are three challenges in using a GPS trajectory as ground truth for camera calibration.<n>The altitude of GPS data is inaccurate with an unknown bias.<n>GPS trajectory is time-discrete and accurate is needed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers a stationary camera calibration problem, which estimates the camera orientation angles yaw, pitch and roll, using a drone trajectory recorded by a GPS. There are three challenges in using a GPS trajectory as ground truth for camera calibration. One, the altitude of GPS data is inaccurate with an unknown bias. Two, the GPS receiver and camera are not time synchronized, and there is an unknown time offset between the two systems. Three, the GPS trajectory is time-discrete and accurate interpolation is needed. This is actually an estimation problem since velocity is also needed. To address the first two challenges, we formulate the problem as a parameter estimation problem to estimate a vector consisting of the GPS altitude bias and time offset in addition to the camera yaw, pitch and roll biases. We then develop a special maximum likelihood estimator using the Iterated Least Squares algorithm which can work with a non-synchronized time-discrete GPS trajectory for the third challenge. Since the camera measurement errors are usually small, this requires a high calibration accuracy so that the residual bias error following the calibration should not be significant compared to the measurement error standard deviation. The calibration accuracy depends highly on the drone trajectory. This paper also recommends an appropriate drone trajectory which can yield a good calibration accuracy, namely, 14\% of the measurement error standard deviation. Simulation tests are conducted to demonstrate the algorithm performance. The estimation results meet the Cramer-Rao Lower Bound (CRLB) since the Normalized Estimation Error Squared w.r.t.\ the CRLB is statistically acceptable.

Related papers

• A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras [51.7262911415172]
  We develop an event-based active marker system to collect real-world data with ground truth from a motion capture system.<n>Experiments on both synthetic and real data demonstrate improved accuracy over the state-of-the-art P2P solver.
  arXiv  Detail & Related papers  (2026-08-10T12:19:24Z)
• Two-Stage Camera Calibration Method for Multi-Camera Systems Using Scene Geometry [52.27010615715154]
  This paper presents a novel two-stage calibration method for multi-camera tracking systems.<n>In the first stage, partial calibration of individual cameras is performed based on an operator's annotation of natural geometric primitives.<n>In the second stage, precise system calibration is achieved through interactive manipulation of the projected Effective Field of View.
  arXiv  Detail & Related papers  (2025-12-04T16:47:39Z)
• UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations [30.56092814783138]
  DF-Calib is a LiDAR-camera calibration method that reformulates calibration as an intra-modality depth flow estimation problem.<n> DF-Calib estimates a dense depth map from the camera image and completes the sparse LiDAR projected depth map.<n>We introduce a reliability map to prioritize valid pixels and propose a perceptually weighted sparse flow loss to enhance depth flow estimation.
  arXiv  Detail & Related papers  (2025-04-02T07:09:44Z)
• Accurate Pose Estimation for Flight Platforms based on Divergent Multi-Aperture Imaging System [8.98211713131741]
  Vision-based pose estimation plays a crucial role in the autonomous navigation of flight platforms.<n>Field of view and spatial resolution of the camera limit pose estimation accuracy.<n>This paper designs a divergent multi-aperture imaging system to achieve simultaneous observation of a large field of view and high spatial resolution.
  arXiv  Detail & Related papers  (2025-02-27T02:51:09Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR [7.906477322731106]
  An accurate extrinsic calibration is required to fuse the camera and LiDAR data into a common spatial reference frame required by high-level perception functions. There is a need for continuous online extrinsic calibration algorithms which can automatically update the value of the camera-LiDAR calibration during the life of the vehicle using only sensor data. We propose using mutual information between the camera image's depth estimate, provided by commonly available monocular depth estimation networks, and the LiDAR pointcloud's geometric distance as a optimization metric for extrinsic calibration.
  arXiv  Detail & Related papers  (2023-06-22T23:16:31Z)
• Feature Tracks are not Zero-Mean Gaussian [63.51990384359593]
  It is customary to assume that the errors in feature track positions are zero-mean Gaussian. Using a combination of calibrated camera intrinsics, ground-truth camera pose, and depth images, it is possible to compute ground-truth positions for feature tracks extracted using an image processing algorithm.
  arXiv  Detail & Related papers  (2023-03-25T00:58:30Z)
• TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching [12.980324010888664]
  We propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space. Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.
  arXiv  Detail & Related papers  (2023-02-04T12:27:01Z)
• What's Wrong with the Absolute Trajectory Error? [14.533304890042361]
  In this work, we propose an alternative error metric for evaluating the accuracy of the reconstructed camera trajectory. Our metric, named Discernible Trajectory Error (DTE), is computed in five steps. We also propose a novel rotation error metric, named Discernible Rotation Error (DRE), which has similar advantages to the DTE.
  arXiv  Detail & Related papers  (2022-12-10T23:48:27Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.