论文标题
基于不确定性感知体素的3D对象检测和von-mises损失的跟踪
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
论文作者
论文摘要
对象检测和跟踪是自治的关键任务。具体而言，3D对象检测和跟踪最近是一个新兴的热门话题。尽管已经提出了各种方法进行对象检测，但是探索了3D检测和跟踪任务的不确定性。不确定性有助于我们解决感知系统中的错误并改善鲁棒性。在本文中，我们提出了一种通过向第二个检测器添加不确定性回归来改善目标跟踪性能的方法，这是3D对象检测的最具代表性算法之一。我们的方法估计了估计值的高斯负模样（NLL）损失的位置和维度不确定性，并引入了Von-Mises NLL损失，以进行角度不确定性估计。我们将不确定性输出提供给经典的对象跟踪框架，并证明我们的方法与持续的协方差假设相比，与香草跟踪器相比提高了跟踪性能。
Object detection and tracking is a key task in autonomy. Specifically, 3D object detection and tracking have been an emerging hot topic recently. Although various methods have been proposed for object detection, uncertainty in the 3D detection and tracking tasks has been less explored. Uncertainty helps us tackle the error in the perception system and improve robustness. In this paper, we propose a method for improving target tracking performance by adding uncertainty regression to the SECOND detector, which is one of the most representative algorithms of 3D object detection. Our method estimates positional and dimensional uncertainties with Gaussian Negative Log-Likelihood (NLL) Loss for estimation and introduces von-Mises NLL Loss for angular uncertainty estimation. We fed the uncertainty output into a classical object tracking framework and proved that our method increased the tracking performance compared against the vanilla tracker with constant covariance assumption.
