# Tightly Coupled 3D Lidar Inertial Odometry and Mapping

## TL;DR

This paper presents a tightly coupled lidar-IMU fusion approach for robust ego-motion estimation in mobile robotics, achieving high-precision pose estimation even in challenging conditions with degraded lidar data.

## Contribution

It introduces a novel joint optimization method for lidar and IMU data fusion and a rotation-constrained refinement algorithm for improved pose accuracy.

## Key findings

- High-precision pose estimation at IMU update rate
- Robust performance under fast motion and feature-scarce conditions
- Effective long-term drift compensation

## Abstract

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.