# LIWO: Lidar-Inertial-Wheel Odometry

## TL;DR

LIWO introduces a novel LiDAR-inertial-wheel odometry system that fuses LiDAR, IMU, and wheel encoder data within a bundle adjustment framework, significantly enhancing state estimation accuracy over existing methods.

## Contribution

The paper presents a new LIO system incorporating wheel encoder data into a BA framework, improving velocity estimation and overall accuracy.

## Key findings

- Outperforms state-of-the-art LIO systems in trajectory accuracy.
- Wheel encoder data significantly enhances LIO performance.
- BA-based optimization benefits from multi-sensor fusion.

## Abstract

LiDAR-inertial odometry (LIO), which fuses complementary information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for state estimation. In LIO, both pose and velocity are regarded as state variables that need to be solved. However, the widely-used Iterative Closest Point (ICP) algorithm can only provide constraint for pose, while the velocity can only be constrained by IMU pre-integration. As a result, the velocity estimates inclined to be updated accordingly with the pose results. In this paper, we propose LIWO, an accurate and robust LiDAR-inertialwheel (LIW) odometry, which fuses the measurements from LiDAR, IMU and wheel encoder in a bundle adjustment (BA) based optimization framework. The involvement of a wheel encoder could provide velocity measurement as an important observation, which assists LIO to provide a more accurate state prediction. In addition, constraining the velocity variable by the observation from wheel encoder in optimization can further improve the accuracy of state estimation. Experiment results on two public datasets demonstrate that our system outperforms all state-of-the-art LIO systems in terms of smaller absolute trajectory error (ATE), and embedding a wheel encoder can greatly improve the performance of LIO based on the BA framework.