天候条件に誘導されたマルチドメイン敵対学習によるループ閉じ込み検出
Condition directed Multi-domain Adversarial Learning for Loop Closure Detection
天候条件を方向付けに用いたGANベースの敵対学習により、雨天・霧・晴天など異なる天候下でも頑健な天候不変特徴を生データから抽出し、ループ閉じ込み検出の精度を向上させた研究。
著者: Peng Yin, Yuqing He, Na Liu, Jianda Han
分類: cs.RO
原文アブストラクト
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.
