[Paper Review] Condition directed Multi-domain Adversarial Learning for Loop Closure Detection

Peng Yin, Yuqing He+2 more|arXiv (Cornell University)|Nov 21, 2017

Advanced Vision and Imaging6 references3 citations

TL;DR

This paper proposes a condition-directed multi-domain adversarial learning framework for loop closure detection (LCD) in visual SLAM, using weather conditions as supervision to learn weather-invariant features via GANs and a classification network. The method outperforms state-of-the-art approaches on the GTAV simulator by effectively capturing high-level, illumination-robust representations from raw images with only weather-condition labels.

ABSTRACT

Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are usually affected by the illumination changes and texture changes under different weather conditions. Traditional methods in LCD usually rely on handcraft features, however, such methods are unable to capture the common descriptions under different weather conditions, such as rainy, foggy and sunny. Furthermore, traditional handcraft features could not capture the highly level understanding for the local scenes. In this paper, we proposed a novel condition directed multi-domain adversarial learning method, where we use the weather condition as the direction for feature inference. Based on the generative adversarial networks (GANs) and a classification networks, the proposed method could extract the high-level weather-invariant features directly from the raw data. The only labels required here are the weather condition of each visual input. Experiments are conducted in the GTAV game simulator, which could generated lifelike outdoor scenes under different weather conditions. The performance of LCD results shows that our method outperforms the state-of-arts significantly.

Motivation & Objective

• To address the challenge of loop closure detection under varying weather and lighting conditions in visual SLAM.
• To learn high-level, weather-invariant visual features without relying on handcrafted descriptors.
• To leverage only weather condition labels for end-to-end feature learning, reducing annotation burden.
• To improve robustness and generalization of LCD in real-world scenarios with dynamic environmental changes.

Proposed method

• The framework employs a conditional generative adversarial network (cGAN) to generate images under different weather conditions, enabling domain-invariant feature learning.
• A classification network is trained to predict the weather condition of input images, guiding the feature extractor to learn weather-robust representations.
• The feature extractor is jointly optimized to minimize domain shift across weather domains while preserving identity consistency for loop-closing instances.
• The model uses adversarial training to encourage the feature space to disentangle content from weather-specific attributes.
• The method trains on raw RGB images without handcrafted features, relying solely on weather condition annotations.
• A multi-domain adversarial loss ensures that features from different weather domains are indistinguishable in the latent space.

Experimental results

Research questions

• RQ1 Can a deep learning model learn weather-invariant visual features for loop closure detection using only weather condition labels?
• RQ2 How does condition-directed multi-domain adversarial learning improve robustness to illumination and weather variations?
• RQ3 To what extent does the proposed method outperform traditional handcrafted feature-based approaches in diverse environmental conditions?
• RQ4 Can adversarial training effectively disentangle content from weather-specific attributes in visual features?

Key findings

• The proposed method significantly outperforms state-of-the-art LCD methods on the GTAV simulator under varied weather conditions.
• The model achieves higher precision and recall in loop closure detection compared to baseline methods, especially in challenging conditions like fog and rain.
• The use of only weather condition labels enables effective feature learning without requiring complex data augmentation or human-annotated scene descriptions.
• The adversarial training scheme successfully reduces domain shift between weather domains, improving generalization across environmental variations.