Pred-NBV: Prediction-Guided Next-Best-View Planning for 3D Object Reconstruction
Pred-NBV: Prediction-Guided Next-Best-View Planning for 3D Object Reconstruction
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DOI：
10.1109/iros55552.2023.10341650
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Harnaik Dhami;V. Sharma;Pratap Tokekar
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文献类型：
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作者：
Harnaik Dhami;V. Sharma;Pratap Tokekar
基于预测的主动感知通过预测未知环境中的不确定性，显示出提高机器人导航效率和安全性的潜力。现有的三维形状预测工作对部分观测值进行了隐式假设，因此不能用于现实世界的规划，也没有考虑次优视图规划的控制努力。我们提出了Pred-NBV，一种现实物体形状重建方法，包括PoinTr-C， ShapeNet数据集训练的增强3D预测模型，以及基于信息和控制努力的次优视图方法来解决这些问题。Pred-NBV在AirSim模拟器中的目标覆盖范围比传统方法提高了25.46%，即使在安装在大疆M600 Pro上的Velodyne 3D激光雷达获得的真实数据上，其形状补全性能也优于最先进的形状补全模型PoinTr。
Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional methods in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.