Vision-Based Navigation I: A navigation filter for fusing DTM/correspondence updates

TL;DR

This paper introduces a vision-based navigation filter that fuses digital terrain map data with image features to improve pose estimation accuracy and robustness, using an extended Kalman filter for integration.

Contribution

It presents a novel algorithm that combines terrain-based constraints with image feature correspondence for enhanced pose and motion estimation.

Findings

Improved robustness and accuracy of navigation using terrain data

Successful numerical simulation validation of the proposed algorithm

Effective fusion of inertial and vision-based navigation results

Abstract

An algorithm for pose and motion estimation using corresponding features in images and a digital terrain map is proposed. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. The utilization of data is shown to improve the robustness and accuracy of the inertial navigation algorithm. Extended Kalman filter was used to combine results of inertial navigation algorithm and proposed vision-based navigation algorithm. The feasibility of this algorithms is established through numerical simulations.