3D object detection with point clouds and images plays an important role in perception tasks such as autonomous driving. Current methods show great performance on detection and pose estimation of standard-shaped vehicles but lack behind on more complex shapes as e.g. semi-trailer truck combinations. Determining the shape and motion of those special vehicles accurately is crucial in yard operation and maneuvering and industrial automation applications. This work introduces several new methods to improve and measure the performance for such classes. State-of-the-art methods are based on predefined anchor grids or heatmaps for ground truth targets. However, the underlying representations do not take the shape of different sized objects into account. Our main contribution, AdaptiveShape, uses shape aware anchor distributions and heatmaps to improve the detection capabilities. For large vehicles we achieve +10.9% AP in comparison to current shape agnostic methods. Furthermore we introduce a new fast LiDAR-camera fusion. It is based on 2D bounding box camera detections which are available in many processing pipelines. This fusion method does not rely on perfectly calibrated or temporally synchronized systems and is therefore applicable to a broad range of robotic applications. We extend a standard point pillar network to account for temporal data and improve learning of complex object movements. In addition we extended a ground truth augmentation to use grouped object pairs to further improve truck AP by +2.2% compared to conventional augmentation.
翻译:使用点云和图像进行 3D 对象探测 3D 点云和图像在自动驾驶等感知任务中起着重要作用。 目前的方法显示在探测和估计标准形状车辆方面表现优异,但缺少诸如半拖车式卡车组合等更复杂的形状。 准确确定这些特殊车辆的形状和运动在院落操作、 操纵和工业自动化应用中至关重要。 这项工作引入了几种新方法来改进和测量这些类别的性能。 国家技术方法基于预先定义的锁定网格或地面真相目标的热图。 但是, 基础显示并不考虑不同大小对象的形状。 我们的主要贡献, 适应式Shape, 使用有意识的锚分布和热图来提高探测能力。 对于大型车辆来说, 我们比照目前形状方法要达到+10.9 % AP。 此外,我们引入了一个新的快速的LIDAR- camera 熔化方法。 它基于2D 装箱相机探测,许多处理管道都可用。 然而, 基础表达方法并不考虑不同大小物体的形状。 我们的主要贡献, 适应于完全校准或时间同步系统和热图, 因此, 我们可以应用一个常规模型的模型的系统, 将一个复杂的地面变换成一个常规的系统。</s>