Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

TL;DR

This paper introduces a channel subset calibration method for CNNs that enhances visual place recognition accuracy and speed by selecting feature maps most relevant to consistent visual features across different appearances.

Contribution

The novel calibration approach selects optimal feature map subsets within CNN layers, improving recognition robustness and computational efficiency across diverse environmental conditions.

Findings

Significant recognition improvements with only 50 calibration images.

Effective across multiple CNN layers and different neural network architectures.

Reduces computational load while maintaining high accuracy.

Abstract

Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of weights that activate for different visual features within the set of training images. We propose a method of calibrating a CNN-based visual place recognition system, which selects the subset of feature maps that best encodes the visual features that are consistent between two different appearances of the same location. Using just 50 calibration images, all collected at the beginning of the current environment, we demonstrate a significant and consistent recognition improvement across multiple layers for two different neural networks. We evaluate our proposal on three datasets with different types of appearance changes - afternoon to morning, winter to summer and night to day. Additionally, the dimensionality reduction approach improves the computational processing speed of the recognition system.