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Object detection and tracking is a key task in autonomy. Specifically, 3Dobject detection and tracking have been an emerging hot topic recently.Although various methods have been proposed for object detection, uncertaintyin the 3D detection and tracking tasks has been less explored. Uncertaintyhelps us tackle the error in the perception system and improve robustness. Inthis paper, we propose a method for improving target tracking performance byadding uncertainty regression to the SECOND detector, which is one of the mostrepresentative algorithms of 3D object detection. Our method estimatespositional and dimensional uncertainties with Gaussian Negative Log-Likelihood(NLL) Loss for estimation and introduces von-Mises NLL Loss for angularuncertainty estimation. We fed the uncertainty output into a classical objecttracking framework and proved that our method increased the trackingperformance compared against the vanilla tracker with constant covarianceassumption.

Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss