ConvNet and LSH-Based Visual Localization Using Localized Sequence Matching

Area of Science:

• Computer Vision
• Robotics
• Machine Learning

Background:

• Convolutional Networks (ConvNets) excel at image representation, driving progress in computer vision and robotics.
• Visual localization is crucial for autonomous systems, but traditional methods struggle with appearance and illumination variations.

Purpose of the Study:

• To propose a novel visual localization approach leveraging ConvNet features and image sequence matching.
• To enhance computational efficiency for real-time performance using Locality Sensitive Hashing (LSH).
• To evaluate the method's robustness against appearance and illumination changes.

Main Methods:

• Extracted ConvNet features to construct an image distance matrix using cosine distance.
• Applied a sequence search technique on the distance matrix for place recognition.
• Utilized Locality Sensitive Hashing (LSH) for accelerated computation and real-time performance.

Main Results:

• The proposed method demonstrates strong performance in visual recognition tasks across four real-world datasets.
• Comprehensive analysis of different ConvNet layers revealed performance trade-offs regarding feature levels.
• The approach proved effective even under significant appearance and illumination variations.

Conclusions:

• The ConvNet-based visual localization method offers a robust and efficient solution for place recognition.
• This technique outperforms traditional methods relying on hand-crafted features and single image matching.
• The integration of ConvNet features with LSH provides a promising direction for real-time visual localization systems.