Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation

TL;DR

This paper presents LOP-UKF, a novel real-time method combining LiDAR odometry and the Pacejka tire model within an Unscented Kalman Filter to accurately estimate vehicle sideslip angle without costly lateral velocity sensors.

Contribution

The paper introduces a new approach integrating LiDAR odometry and tire modeling with UKF for lateral velocity estimation, offering a robust alternative to traditional sensor-dependent methods.

Findings

Effective sideslip estimation across diverse conditions

Reliable performance in edge cases

Validated on Dallara AV-21 vehicle

Abstract

The sideslip angle, crucial for vehicle safety and stability, is determined using both longitudinal and lateral velocities. However, measuring the lateral component often necessitates costly sensors, leading to its common estimation, a topic thoroughly explored in existing literature. This paper introduces LOP-UKF, a novel method for estimating vehicle lateral velocity by integrating Lidar Odometry with the Pacejka tire model predictions, resulting in a robust estimation via an Unscendent Kalman Filter (UKF). This combination represents a distinct alternative to more traditional methodologies, resulting in a reliable solution also in edge cases. We present experimental results obtained using the Dallara AV-21 across diverse circuits and track conditions, demonstrating the effectiveness of our method.