# EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images

## TL;DR

This paper introduces EKFPnP, a method using an Extended Kalman Filter to improve camera pose estimation over sequences of images by accounting for temporal dependencies and feature uncertainties, enhancing robustness against noise.

## Contribution

The paper presents a novel EKF-based approach for sequential camera pose estimation that considers feature uncertainty and temporal dependencies, improving robustness over existing methods.

## Key findings

- Enhanced robustness in noisy conditions
- Effective in both simulated and real data
- Outperforms state-of-the-art methods

## Abstract

In real-world applications the Perspective-n-Point (PnP) problem should generally be applied in a sequence of images which a set of drift-prone features are tracked over time. In this paper, we consider both the temporal dependency of camera poses and the uncertainty of features for the sequential camera pose estimation. Using the Extended Kalman Filter (EKF), a priori estimate of the camera pose is calculated from the camera motion model and then corrected by minimizing the reprojection error of the reference points. Experimental results, using both simulated and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of noise, compared to the state-of-the-art.