Stereo Matching for Martian Surface Depth Estimation on a Single-Board Computer

Master thesis (2021)

Authors

C. Lau Aerospace Engineering

Contributors

S. Speretta Space Systems Egineering (supervisor 1)
J. Guo Space Systems Egineering (supervisor 2)
B.C. Root Astrodynamics & Space Missions - Aerospace Engineering (supervisor 2)
Faculty
Aerospace Engineering
Copyright: © 2021 Chy Lau
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Published Date

31-08-2021

Language

English

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Faculty

Aerospace Engineering

Abstract

Stereo matching, the process of inferring depth maps from stereo images, is one of the most heavily investigated topics in computer vision. It is part of the first module of navigation systems of planetary rovers, e.g., NASA’s Mars Exploration Rover (MER) missions, NASA’s Mars Science Laboratory (MSL) mission, and ESA’s ExoMars mission. Many stereo matching algorithms, traditional and deep learning based, are available and their performance is typically evaluated on indoor objects or outdoor city scenes. In this thesis, our goal is to improve insight into the performance of stereo matching algorithms for the Martian surface on a single-board computer. First, we search for and compare stereo matching algorithms ranked on stereo vision datasets to obtain a manageable set of algorithms and verify their implementations. Second, we derive performance metrics from the requirements and validate our set of verified algorithms on the Katwijk Beach Planetary Rover dataset. Through experimental evaluation, we gain insight into the performance of four stereo matching algorithms.