Vision-Based Navigation III: Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map

TL;DR

This paper presents an algorithm for pose and motion estimation using omnidirectional images and digital terrain maps, extending previous methods to non-central projection systems to improve robustness and accuracy.

Contribution

It extends existing pose estimation algorithms to handle non-central projection omnidirectional systems using digital terrain maps.

Findings

Omnidirectional data enhances navigation robustness.

Lab experiments confirm algorithm feasibility with polydioptric and catadioptric cameras.

The method improves pose accuracy over traditional approaches.

Abstract

An algorithm for pose and motion estimation using corresponding features in omnidirectional images and a digital terrain map is proposed. In previous paper, such algorithm for regular camera was considered. 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. In this paper, these constraints are extended to handle non-central projection, as is the case with many omnidirectional systems. The utilization of omnidirectional data is shown to improve the robustness and accuracy of the navigation algorithm. The feasibility of this algorithm is established through lab experimentation with two kinds of omnidirectional acquisition systems. The first one is polydioptric cameras while the second is catadioptric camera.