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Yaw estimation of boats from the viewpoint of unmanned aerial vehicles (UAVs)and unmanned surface vehicles (USVs) or boats is a crucial task in variousapplications such as 3D scene rendering, trajectory prediction, and navigation.However, the lack of literature on yaw estimation of objects from the viewpointof UAVs has motivated us to address this domain. In this paper, we propose amethod based on HyperPosePDF for predicting the orientation of boats in the 6Dspace. For that, we use existing datasets, such as PASCAL3D+ and our owndatasets, SeaDronesSee-3D and BOArienT, which we annotated manually. We extendHyperPosePDF to work in video-based scenarios, such that it yields robustorientation predictions across time. Naively applying HyperPosePDF on videodata yields single-point predictions, resulting in far-off predictions andoften incorrect symmetric orientations due to unseen or visually differentdata. To alleviate this issue, we propose aggregating the probabilitydistributions of pose predictions, resulting in significantly improvedperformance, as shown in our experimental evaluation. Our proposed method couldsignificantly benefit downstream tasks in marine robotics.

Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs