MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
Journal:
arXiv
Published Date:
Dec 5, 2024
Abstract
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.
