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.
翻译:3D天体探测等机器人视觉不确定性的估计是开发了解自身性能的安全自主系统的一个必要组成部分。然而,由于时间和计算限制,在3D天体探测中采用目前的不确定性估计方法仍然具有挑战性。为解决这一问题,我们提议使用多投入多输出量(MIMO)不确定性估计方法来适应基于3DD天体探测任务。我们的方法通过在地物一级进行多投入来改变原MIMO,以确保探测、不确定性估计和运行时间性能效益得以保留,尽管基础探测器的能力有限,点云处理计算成本也很高。我们将LIDAR-MIMO与MC的辍学和聚合作为基线,并显示类似的不确定性估计结果,只有少量产出头。此外,LIDAR-MIMO的配置可以比MC的辍学和聚合速度高出一倍,同时取得高于MC的辍学率和接近于圆云堆的接近。