Analytical Models for Pose Estimate Variance of Planar Fiducial Markers for Mobile Robot Localisation

Abstract

Planar fiducial markers are commonly used to estimate a pose of a camera relative to the marker. This information can be combined with other sensor data to provide a global or local position estimate of the system in the environment using a state estimator such as the Kalman filter. To achieve accurate estimates, the observation noise covariance matrix must be properly configured to reflect the sensor output’s characteristics. However, the observation noise of the pose obtained from planar fiducial markers varies across the measurement range and this fact needs to be taken into account during the sensor fusion to provide a reliable estimate. In this work, we present experimental measurements of the fiducial markers in real and simulation scenarios for 2D pose estimation. Based on these measurements, we propose analytical functions that approximate the variances of pose estimates. We demonstrate the effectiveness of our approach in a 2D robot localisation experiment, where we present a method for estimating covariance model parameters based on user measurements and a technique for fusing pose estimates from multiple markers.

Multi-Tag Fusion Localization Method Based on Geometric Constraints

“…However, ref. [27] discovered that the observation noise of pose from planar fiducial markers varies across the measurement range, a fact that must be considered during sensor fusion for a reliable estimate. They present experimental measurements of fiducial markers in real and simulation scenarios for 2D pose estimation and propose analytical functions that approximate the variances of pose estimates.…”

The Archimede Rover: A Comparison Between Simulations and Experiments

“…One of the key challenges in planetary exploration is position estimation [51]. Since outside of the Earth's surface global positioning is not currently available, rovers must rely on relative position estimation, where positioning is typically achieved by means of dead reckoning techniques [52], possibly enhanced by sensor fusion, where information coming from different sensors is fused together, typically by leveraging the Extended Kalman Filter (EKF) [53] or Bayesian approaches [54]. An example of this can be found in planetary rovers that use positioning systems that fuse readings from of the following: wheel encoders (wheels odometry-WO), Inertial Measurement Units (IMU) [55], sun sensors [51], and visual odometry (VO) [56].…”