论文标题
MLOD：自动驾驶的多LIDAR 3D对象检测中外部扰动的意识
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
论文作者
论文摘要
外部扰动总是存在于多个传感器中。在本文中，我们专注于用于3D对象检测的多弹药系统中的外部不确定性。我们首先通过两个基本示例分析外部扰动对几何任务的影响。为了最大程度地减少外部扰动的有害效果，我们在每个输入点云的每个点上都先验不确定性，并使用此信息来增强3D几何任务的方法。然后，我们扩展了发现，以提出一个称为MLOD的多LIDAR 3D对象检测器。 MLOD是一个两阶段的网络，其中通过第一阶段的各种方案融合了多LALAR信息，并且在第二阶段中处理外部扰动。我们在现实世界数据集上进行了广泛的实验，并展示了MLOD的准确性和鲁棒性提高。代码，数据和补充材料可在以下网址获得：https：//ram-lab.com/file/site/mlod
Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the influence of extrinsic perturbation on geometric tasks with two basic examples. To minimize the detrimental effect of extrinsic perturbation, we propagate an uncertainty prior on each point of input point clouds, and use this information to boost an approach for 3D geometric tasks. Then we extend our findings to propose a multi-LiDAR 3D object detector called MLOD. MLOD is a two-stage network where the multi-LiDAR information is fused through various schemes in stage one, and the extrinsic perturbation is handled in stage two. We conduct extensive experiments on a real-world dataset, and demonstrate both the accuracy and robustness improvement of MLOD. The code, data and supplementary materials are available at: https://ram-lab.com/file/site/mlod
