Adaptive Smoothing for Trajectory Reconstruction

TL;DR

This paper introduces an adaptive V-spline smoothing method for reconstructing trajectories that effectively handles irregular sampling and noisy velocity data, with demonstrated success on challenging datasets and vehicle applications.

Contribution

It presents a novel adaptive V-spline approach that integrates position and velocity, with a new parameter estimation scheme for improved trajectory reconstruction.

Findings

Effective on challenging datasets

Handles irregular sampling and noise

Applicable to vehicle trajectory reconstruction

Abstract

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline -- which we name the V-spline -- that incorporates position and velocity information and a penalty term that controls acceleration. We introduce a particular adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is given and we detail the performance of the V-spline on four particularly challenging test datasets. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.