This paper presents a 6-DOF relocalization using a 3D laser scanner and a monocular camera. A relocalization problem in robotics is to estimate pose of sensor when a robot revisits the area. A deep convolutional neural network (CNN) is designed to regress 6-DOF sensor pose and trained using both RGB image and 3D point cloud information in end-to-end manner. We generate the new input that consists of RGB and range information. After training step, the relocalization system results in the pose of the sensor corresponding to each input when a new input is received. However, most of cases, mobile robot navigation system has successive sensor measurements. In order to improve the localization performance, the output of CNN is used for measurements of the particle filter that smooth the trajectory. We evaluate our relocalization method on real world datasets using a mobile robot platform.

Abstract
 1. 서 론
 2. 선행 연구 조사
 3. 심층 컨벌루션 신경망 기반 위치 재인식
  3.1 센서 융합 시스템
  3.2 네트워크 구조
  3.3 위치 회귀 모델 및 파티클 필터 기반 위치인식
 4. 실험 및 결과
  4.1 데이터셋
  4.2 위치인식 결과
 5. 결 론
 References

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• 조형기(Dept. of Electrical and Electronic Engineering, Yonsei University) | HyungGi Jo
• 조해민(Dept. of Electrical and Electronic Engineering, Yonsei University) | Hae Min Cho
• 이성원(Dept. of Electrical and Electronic Engineering, Yonsei University) | Seongwon Lee
• 김은태(Dept. of Electrical and Electronic Engineering, Yonsei University) | Euntai Kim Corresponding author