MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
This dataset addresses the need for robust sensing in maritime autonomous navigation, particularly for overcoming challenges like harsh weather and long detection ranges, though it is incremental as it builds on existing sensor integration approaches.
The authors tackled the problem of maritime environmental sensing by creating MOANA, a multi-radar dataset that integrates short-range LiDAR, medium-range W-band radar, and long-range X-band radar data with object labels for oceanic object detection. The dataset includes seven sequences from diverse regions with varying difficulty levels, providing a resource for research in place recognition, odometry estimation, SLAM, object detection, and dynamic object elimination in maritime environments.
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 at https://sites.google.com/view/rpmmoana.