MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application

Maritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic objects, and the requirement for long detection ranges. While cameras and LiDAR are commonly used in ground vehicle navigation, their applicability in maritime settings is limited by range constraints and hardware maintenance issues. Radar sensors, however, offer robust long-range detection capabilities and resilience to physical contamination from weather and saline conditions, making it a powerful sensor for maritime navigation. Among various radar types, X-band radar is widely employed for maritime vessel navigation, providing effective long-range detection essential for situational awareness and collision avoidance. Nevertheless, it exhibits limitations during berthing operations where near-field detection is critical. To address this shortcoming, we incorporate W-band radar, which excels in detecting nearby objects with a higher update rate. We present a comprehensive maritime sensor dataset featuring multi-range detection capabilities. This dataset integrates short-range LiDAR data, medium-range W-band radar data, and long-range X-band radar data into a unified framework. Additionally, it includes object labels for oceanic object detection usage, derived from radar and stereo camera images. The dataset comprises seven sequences collected from diverse regions with varying levels of \bl{navigation algorithm} estimation difficulty, ranging from easy to challenging, and includes common locations suitable for global localization tasks. This dataset serves as a valuable resource for advancing research in place recognition, odometry estimation, SLAM, object detection, and dynamic object elimination within maritime environments. Dataset can be found atthis https URL.

@article{jang2025_2412.03887,
  title={ MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application },
  author={ Hyesu Jang and Wooseong Yang and Hanguen Kim and Dongje Lee and Yongjin Kim and Jinbum Park and Minsoo Jeon and Jaeseong Koh and Yejin Kang and Minwoo Jung and Sangwoo Jung and Chng Zhen Hao and Wong Yu Hin and Chew Yihang and Ayoung Kim },
  journal={arXiv preprint arXiv:2412.03887},
  year={ 2025 }
}