LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map

TL;DR

LIO-GVM introduces a tightly-coupled lidar-inertial odometry framework that uses Gaussian voxel maps and a novel residual metric to improve localization accuracy and robustness in various environments.

Contribution

It proposes a new residual metric based on variance divergence and a voxel-only mapping scheme for enhanced lidar-inertial odometry performance.

Findings

Demonstrates improved accuracy over existing methods

Shows robustness across diverse datasets and environments

Provides open-source implementation for community use

Abstract

This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching, we represent the points as a set of Gaussian distributions and evaluate the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry, which demonstrates an improvement from merely quantifying distance to incorporating variance disparity, further enriching the comprehensiveness and accuracy of the residual metric. Due to the strategic design of the residual metric, we propose a simple yet effective voxel-solely mapping scheme, which only necessities the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and accuracy of our framework for various data inputs and environments. To the benefit of the robotics society, we open source the code at https://github.com/Ji1Xingyu/lio_gvm.