LIR-LIVO: A Lightweight,Robust LiDAR/Vision/Inertial Odometry with Illumination-Resilient Deep Features

TL;DR

LIR-LIVO is a lightweight, robust odometry system combining LiDAR, visual, and inertial data with deep learning features to perform accurately in challenging lighting and degraded environments.

Contribution

It introduces a novel illumination-resilient feature extraction method integrated with LiDAR-inertial-visual odometry, achieving state-of-the-art accuracy with low computational cost.

Findings

Outperforms existing methods on benchmark datasets.

Demonstrates robustness under poor lighting conditions.

Achieves high accuracy with low computational complexity.

Abstract

In this paper, we propose LIR-LIVO, a lightweight and robust LiDAR-inertial-visual odometry system designed for challenging illumination and degraded environments. The proposed method leverages deep learning-based illumination-resilient features and LiDAR-Inertial-Visual Odometry (LIVO). By incorporating advanced techniques such as uniform depth distribution of features enabled by depth association with LiDAR point clouds and adaptive feature matching utilizing Superpoint and LightGlue, LIR-LIVO achieves state-of-the-art (SOTA) accuracy and robustness with low computational cost. Experiments are conducted on benchmark datasets, including NTU-VIRAL, Hilti'22, and R3LIVE-Dataset. The corresponding results demonstrate that our proposed method outperforms other SOTA methods on both standard and challenging datasets. Particularly, the proposed method demonstrates robust pose estimation under poor ambient lighting conditions in the Hilti'22 dataset. The code of this work is publicly accessible on GitHub to facilitate advancements in the robotics community.