论文标题
LIDAR-MIMO：基于激光雷达的3D对象检测的有效不确定性估计
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
论文作者
论文摘要
机器人视觉中不确定性（例如3D对象检测）的估计是开发意识到自己性能的安全自主系统的重要组成部分。但是，由于时间和计算限制，3D对象检测中当前不确定性估计方法的部署仍然具有挑战性。为了解决此问题，我们建议LIDAR-MIMO，这是对基于激光雷达的3D对象检测任务的多输入多输出（MIMO）不确定性估计方法的改编。我们的方法通过在功能级别上执行多输入来修改原始MIMO，以确保尽管基础检测器的容量有限以及点云处理的大量计算成本，但仍保留了检测，不确定性估计和运行时性能益处。我们将LiDAR-MIMO与MC脱落和集合作为基准进行比较，并显示出可比的不确定性估计结果，而仅少量输出头。此外，LIDAR-MIMO可以配置为MC脱落和合奏的两倍，同时获得比MC脱落更高的地图并接近合奏的地图。
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance. However, the deployment of current uncertainty estimation methods in 3D object detection remains challenging due to timing and computational constraints. To tackle this issue, we propose LiDAR-MIMO, an adaptation of the multi-input multi-output (MIMO) uncertainty estimation method to the LiDAR-based 3D object detection task. Our method modifies the original MIMO by performing multi-input at the feature level to ensure the detection, uncertainty estimation, and runtime performance benefits are retained despite the limited capacity of the underlying detector and the large computational costs of point cloud processing. We compare LiDAR-MIMO with MC dropout and ensembles as baselines and show comparable uncertainty estimation results with only a small number of output heads. Further, LiDAR-MIMO can be configured to be twice as fast as MC dropout and ensembles, while achieving higher mAP than MC dropout and approaching that of ensembles.
