Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).

1. 서 론
 2. 선행 연구 조사
 3. 사이드 스캔 소나 pose-graph SLAM
  3.1 사이드 스캔 소나 모델링
  3.2 사이드 스캔 소나와 에코사운더 데이터 융합
  3.3 사이드 스캔 소나 번들 조정
  3.4 도플러 속도 로그(DVL)센서와 관성 측정 유닛(IMU)를 이용한 추측 항법 경로 생성
  3.5 사이드 스캔 소나 기반 SLAM 알고리즘 개요
 4. 시뮬레이션
  4.1 사이드 스캔 소나 모듈 시뮬레이션 설정
  4.2 A-KAZE 특징점 기반 이미지 매칭
  4.3 인공 환경에서의 사이드 스캔 소나 번들 조정
  4.4 iSAM를 사용한 pose-graph 최적화
 5. 결 론
 References

• 권대현(Dept. of Civil and Environmental Engineering, KAIST) | Dae-Hyeon Gwon
• 김주완(Dept. of Civil and Environmental Engineering, KAIST) | Joowan Kim
• 김문환(Maritime R&D Lab, LIG Nex1 Co. Ltd) | Moon Hwan Kim
• 박호규(Maritime R&D Lab, LIG Nex1 Co. Ltd) | Ho Gyu Park
• 김태영(Maritime R&D Lab, LIG Nex1 Co. Ltd) | Tae Yeong Kim
• 김아영(Dept. of Civil and Environmental Engineering, KAIST) | Ayoung Kim Corresponding author