  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.
