Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
This work addresses uncertainty estimation for robustness in 3D perception systems, primarily for autonomous applications, but it is incremental as it builds on an existing detector and tracking framework.
The paper tackles the problem of uncertainty in 3D object detection and tracking by adding uncertainty regression to the SECOND detector, using Gaussian and von-Mises losses, and shows that this method improves tracking performance compared to a baseline tracker.
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.