# Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization

arXiv: https://arxiv.org/abs/2503.23199 · HTML: https://mlsanity.com/paper/2503.23199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes](https://mlsanity.com/paper/1711.05805/) (2017)
- [Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences](https://mlsanity.com/paper/2004.00740/) (2020)
- [MF-MOS: A Motion-Focused Model for Moving Object Segmentation](https://mlsanity.com/paper/2401.17023/) (2024)
- [LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps](https://mlsanity.com/paper/2007.01595/) (2020)
- [A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces](https://mlsanity.com/paper/1904.12116/) (2019)
- [Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation](https://mlsanity.com/paper/2007.14759/) (2020)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments](https://mlsanity.com/paper/2009.02622/) (2020)
- [DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots](https://mlsanity.com/paper/2103.06112/) (2021)
- [Monocular Direct Sparse Localization in a Prior 3D Surfel Map](https://mlsanity.com/paper/2002.09923/) (2020)
- [ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM](https://mlsanity.com/paper/2109.05483/) (2021)
- [Real-Time, Environmentally-Robust 3D LiDAR Localization](https://mlsanity.com/paper/1910.12728/) (2019)

## Abstract (arXiv)

Currently, visual odometry and LIDAR odometry are performing well in pose estimation in some typical environments, but they still cannot recover the localization state at high speed or reduce accumulated drifts. In order to solve these problems, we propose a novel LIDAR-based localization framework, which achieves high accuracy and provides robust localization in 3D pointcloud maps with information of multi-sensors. The system integrates global information with LIDAR-based odometry to optimize the localization state. To improve robustness and enable fast resumption of localization, this paper uses offline pointcloud maps for prior knowledge and presents a novel registration method to speed up the convergence rate. The algorithm is tested on various maps of different data sets and has higher robustness and accuracy than other localization algorithms.