Adaptive Thresholding for Sequence-Based Place Recognition

Robots need to know where they are in the world to operate effectively without human support. One common first step for precise robot localization is visual place recognition. It is a challenging problem, especially when the output is required in an online fashion, and the current state-of-the-art approaches that tackle it usually require either large amounts of labeled training data or rely on parameters that need to be tuned manually, often per dataset. One such parameter often used for sequence-based place recognition is the image similarity threshold that allows to differentiate between pairs of images that represent the same place even in the presence of severe environmental and structural changes, and those that represent different places even if they share a similar appearance. Currently, selecting this threshold is a manual procedure and requires human expertise. We propose an automatic similarity threshold selection technique and integrate it into a complete sequence-based place recognition system. The experiments on a broad range of real-world and simulated data show that our approach is capable of matching image sequences under various illumination, viewpoint and underlying structural changes, runs online, and requires no manual parameter tuning while yielding performance comparable to a manual, dataset-specific parameter tuning. Thus, this paper substantially increases the ease of use of visual place recognition in real-world settings.

Authors

• Olga Vysotska
• Igor Bogoslavskyi
• Marco Hutter 0001
• Cyrill Stachniss

Keywords

• Image recognition
• Statistical analysis
• Training data
• Lighting
• Manuals
• Robot localization
• Image sequences
• Tuning
• Robots
• Visual place recognition
• Adaptive Threshold
• Place Recognition
• Environmental Changes
• Image Registration
• Real-world Data
• Presence Of Changes
• Image Pairs
• Threshold Selection
• Automatic Selection
• Manual Tuning
• Online Fashion
• Normal Distribution
• Reference Genome
• Kolmogorov-Smirnov Test
• Distribution Of Values
• F1 Score
• Similarity Matrix
• Query Sequence
• Continuous Change
• Adaptive Approach
• Good Threshold
• Discrete Changes
• Gaussian Mixture Model
• Loop Closure
• Query Image
• Cumulative Density Function
• Deployment Scenarios
• Sequence-based Approaches
• Adaptive Selection
• Illumination Changes