Autonomous Vehicle Calibration via Linear Optimization

TL;DR

This paper introduces a linear optimization-based pipeline for calibrating autonomous vehicle odometry parameters, effectively reducing error accumulation and improving navigation accuracy with small datasets.

Contribution

It presents a novel calibration method using sequential least square programming to optimize kinematic parameters of autonomous vehicles.

Findings

Accurate calibration achieved with small datasets.

Reduced error accumulation in vehicle odometry.

Effective for various landmark-based navigation scenarios.

Abstract

In navigation activities, kinematic parameters of a mobile vehicle play a significant role. Odometry is most commonly used for dead reckoning. However, the unrestricted accumulation of errors is a disadvantage using this method. As a result, it is necessary to calibrate odometry parameters to minimize the error accumulation. This paper presents a pipeline based on sequential least square programming to minimize the relative position displacement of an arbitrary landmark in consecutive time steps of a kinematic vehicle model by calibrating the parameters of applied model. Results showed that the developed pipeline produced accurate results with small datasets.