  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.
