M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance

TL;DR

This paper introduces M-LIO, a robust multi-lidar and multi-IMU odometry system that effectively fuses sensor data with GNSS, handling signal dropout and improving localization accuracy in real-time.

Contribution

The paper presents a novel sensor fusion framework that integrates multiple lidars, IMUs, and GNSS with dropout tolerance, using a factor graph approach for real-time state estimation.

Findings

61% improvement in relative translation error

42% reduction in rotational error

Validated on real vehicle data with significant accuracy gains

Abstract

We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform incremental motion in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair.