MariSense: A multi-modal dataset for environmental perception in maritime autonomy
MariSense: A multi-modal dataset for environmental perception in maritime autonomy
This publication presents MariSense, the first multi-modal dataset for maritime autonomy perception that introduces dynamic waveform 4D mmWave radar alongside visible, thermal, LiDAR, marine radar, AIS, and navigation sensors. The dataset covers anchorages and fairways under varying weather, time, and sea conditions, with extrinsically calibrated sensors. It establishes benchmarks for object detection, tracking, and odometry and evaluates baseline methods to highlight needs in maritime perception, multi-modal fusion, and adaptive radar waveform design. The dataset is available at https://github.com/ORCAUBOAT/MariSense.
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The paper introduces MariSense, a multi-modal dataset for maritime autonomy that integrates dynamic waveform 4D mmWave radar with visible/thermal cameras, LiDAR, marine radar, AIS, and navigation sensors; it covers anchorages and fairways under varying weather, time, and sea conditions and establishes benchmarks for object detection, tracking, and odometry to evaluate baseline methods.
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Maritime autonomous navigation enables unmanned surface vehicles to perform multiple tasks and can also serve as a driver-assistance system for manned vessels, improving safety and comfort. Compared with on-road scenes, maritime perception faces unique challenges: optical sensors degrade due to reflections and sea fog, while wind and waves cause large platform motions that disrupt perception stability. Therefore, hig
Open source S2
h-quality multi-modal datasets are crucial. Meanwhile, 4D millimeter-wave (mmWave) radar has been successfully applied in on-road scenes due to its all-weather, all-day, and high-resolution sensing capabilities, but its maritime characteristics and waveform design effects remain underexplored. To address these challenges, we present MariSense, a multi-modal dataset for maritime autonomy perception. MariSense is the f
Open source S3
irst to introduce the concept of dynamic waveform 4D mmWave radar into maritime scenes, integrating multi-waveform radars with visible and thermal cameras, LiDAR, marine radar, Automatic Identification System (AIS), and navigation sensors, all with extrinsic calibration. The dataset covers various scenes such as anchorages and fairways under varying weather, time, and sea conditions. We establish benchmarks for objec
Open source S4
MariSense: A multi-modal dataset for environmental perception in maritime autonomy
Open source S1
t detection, tracking, and odometry, and evaluate baseline methods, highlighting the need for improved maritime perception, multi-modal fusion, and adaptive 4D mmWave radar waveform design. The dataset can be downloaded from https://github.com/ORCAUBOAT/MariSense .
Open source S5
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Source：International Journal of Robotics Research — Journal metadata · doi.org