Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

Abstract
 1. 서 론
 2. 관련 연구
 3. 환경 변화에 강인한 장소 인식 방법
  3.1 Convolutional Auto-encoder 구조
  3.2 CAE를 이용한 특징 추출 및 장소 인식 방법
 4. 실험 결과
  4.1 실험 환경
  4.2 이미지 생성 실험
  4.3 장소 인식 성능 실험
 5. 결 론
 References

• 오정현(Electrical and Computer Engineering, Seoul National University) | Junghyun Oh Corresponding author
• 이범희(Electrical and Computer Engineering, Seoul National University) | Beomhee Lee