Vehicle trajectory estimation for the generation of ground truth data

Abstract

Techniques are disclosed to enable the generation of ground truth datasets that identify a continuous time movement trajectory of dynamic objects such as non-stationary vehicles. The techniques as described herein may utilize iterative closest point (ICP) processes to compute a rigid-body transform that describes a 3D correspondence between different pairs of temporal point cloud data sample sets at various discrete time periods that are identified with an object along a movement trajectory. From these rigid-body transforms at each of the discrete time periods, a pose graph optimization may be performed to compute the continuous time movement trajectory. The ground truth datasets may be used to enable training of machine learning models, which may be used by a vehicle computing system when navigating a driving environment.