3D-DUO: 3D detection of underwater objects in low-resolution multibeam echosounder maps

• We train a transformation-equivariant 3D detection model on low-resolution pointcloud maps collected using a MBES. • We perform 3D detection of objects in pointclouds, predicting their positions and orientations. • We generate synthetic and real datasets which include a variety of geometric objects to investigate the effect of pointcloud sparsity and the addition of synthetic noise on the detection performance. • We show the generalizability of the proposed method to detect objects in real MBES maps while being trained on synthetic data only. Exploration of the underwater domain has always been a challenging task for researchers to tackle. Perception being a major part of this exploration, requires robust systems and sensors to perform accurate mapping, detection, tracking, and 3d-reconstruction of the underwater medium. Although many use optical sensors for such tasks, they get highly affected by the reduced visibility and high turbidity causing the loss of essential features in captured scenes. This work proposes the use of a deep-learning model for acoustic-based 3D object detection. The idea is to detect regions of interest/objects in sparse multibeam echo sounder 3D maps which are low in resolution and consist of minimum amount of features. Since acquiring data from a real environment is hard, the model was trained on a synthetically generated datasets consisting of several objects. Simulation experiments showed promising results performing successful 3D object detection, which is then further tested in a real experiment on seven different objects. The system performed accurate 3D detection achieving an mAP of 0.75.