Evaluation and Optimization of Feature Detectors Towards Off-Road Visual Odometry
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Feature detectors are used in many computer vision applications, including Visual Odometry
(VO). However, their utilization in off-road VO remains a topic of great interest. In this body
of work, software tools are developed in order to evaluate and parameter-optimize feature detectors
towards real-time off-road VO.
Using the tools developed, various classical as well as state-of-the-art machine learning-based
feature detectors are evaluated and optimized, including their usage in a pre-existing VO implementation
and analyzing the output trajectory. The analysis and results presented show that although
the quality of feature detectors have an impact, optimizing them alone cannot overcome
the inherent drawbacks of monocular VO approaches. Based on the analysis, recommendations
of potential feature detectors that can support real-time off-road VO are made and optimized
parameters of selected feature detectors are provided. Key areas of improvement for future research
in the field are also identified.
Description
Keywords
Computer Vision, Feature Detection, Visual Odometry