Exploiting Structure-from-Motion for Robust Vision-Based Map Matching for Aircraft Surface Movement

TL;DR

This paper presents a vision-aided navigation pipeline that combines feature-based SfM and image matching to improve ground navigation accuracy for autonomous aircraft, with robustness features for safety.

Contribution

It introduces a novel hybrid approach combining indirect and direct image methods for aircraft ground navigation, emphasizing robustness and anomaly detection.

Findings

SfM drift challenges accuracy in map matching

Pipeline can identify registration anomalies

Provides a robust solution for autonomous aircraft movement

Abstract

In this paper we introduce a vision-aided navigation (VAN) pipeline designed to support ground navigation of autonomous aircraft. The proposed algorithm combines the computational efficiency of indirect methods with the robustness of direct image-based techniques to enhance solution integrity. The pipeline starts by processing ground images (e.g., acquired by a taxiing aircraft) and relates them via a feature-based structure-from-motion (SfM) solution. A ground plane mosaic is then constructed via homography transforms and matched to satellite imagery using a sum of squares differences (SSD) of intensities. Experimental results reveal that drift within the SfM solution, similar to that observed in dead-reckoning systems, challenges the expected accuracy benefits of map-matching with a wide-baseline ground-plane mosaic. However, the proposed algorithm demonstrates key integrity features, such as the ability to identify registration anomalies and ambiguous matches. These characteristics of the pipeline can mitigate outlier behaviors and contribute toward a robust, certifiable solution for autonomous surface movement of aircraft.