Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold

Area of Science:

• Robotics
• Computer Vision
• Machine Learning

Background:

• Appearance-based robot localization relies on visual cues.
• Existing methods struggle with appearance variations and require dense maps.

Purpose of the Study:

• To develop a robust and accurate appearance-based robot localization system.
• To create a lightweight and efficient environment map for localization.

Main Methods:

• Proposed a novel 'appearance map' using Patches of Smooth Appearance Change (PSACs).
• Employed a Gaussian Process Particle Filter (GPPF) for camera tracking and Place Recognition (PR) for relocalization.
• Utilized Gaussian Processes (GPs) trained per PSAC for descriptor regression and likelihood estimation.

Main Results:

• Achieved median localization errors below 0.3 m and 6°.
• Demonstrated superior robustness and accuracy compared to state-of-the-art methods.
• Successfully handled appearance variations using GP white noise modeling.

Conclusions:

• The proposed method offers a significant advancement in appearance-based robot localization.
• The PSAC-based appearance map provides an efficient and effective representation for localization.
• The GPPF and GP-based approach ensures reliable performance under changing environmental conditions.