Analyzing the Impact of Objects in an Image on Location Estimation Accuracy in Visual Localization.

Area of Science:

• Computer Vision
• Robotics
• Geographic Information Systems

Background:

• Visual localization determines an observer's pose using image feature relationships.
• Accurate pose estimation relies heavily on precise feature matching between images.
• Incorrect feature matches can significantly degrade localization accuracy.

Purpose of the Study:

• To evaluate the reliability of different object classes for visual localization.
• To investigate the impact of unreliable object classes on pose estimation accuracy.
• To provide insights for constructing better datasets for visual localization.

Main Methods:

• Assessed pose estimation accuracy across various object classes in image datasets.
• Identified 'building' as a reliable class and 'human' as unreliable.
• Artificially increased the proportion of 'human' objects to study accuracy degradation.

Main Results:

• The 'building' object class demonstrated reliability for pose estimation.
• The 'human' object class showed unreliability across different locations.
• Pose estimation accuracy significantly declined when 'human' objects exceeded 20% of images.

Conclusions:

• Object class reliability is crucial for accurate visual localization.
• Dataset composition, particularly the prevalence of humans, directly impacts localization performance.
• Recommendations for dataset construction to improve visual localization accuracy.