LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

TL;DR

This paper introduces a tightly coupled sensor fusion method combining UWB, Lidar, and IMU data to improve robot localization accuracy and reduce drift, demonstrated through real-world experiments.

Contribution

The paper presents a novel fusion scheme integrating UWB, Lidar, and IMU measurements in a unified optimization framework for robust odometry.

Findings

Effectively reduces estimation drift in robot localization

Requires only two or three anchors for accurate positioning

Demonstrates improved accuracy in real-world experiments

Abstract

In recent years, thanks to the continuously reduced cost and weight of 3D Lidar, the applications of this type of sensor in robotics community have become increasingly popular. Despite many progresses, estimation drift and tracking loss are still prevalent concerns associated with these systems. However, in theory these issues can be resolved with the use of some observations to fixed landmarks in the environments. This motivates us to investigate a tightly coupled sensor fusion scheme of Ultra-Wideband (UWB) range measurements with Lidar and inertia measurements. First, data from IMU, Lidar and UWB are associated with the robot's states on a sliding windows based on their timestamps. Then, we construct a cost function comprising of factors from UWB, Lidar and IMU preintegration measurements. Finally an optimization process is carried out to estimate the robot's position and orientation. Via some real world experiments, we show that the method can effectively resolve the drift issue, while only requiring two or three anchors deployed in the environment.