A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry

[IEEE RA-L & ICRA'22] A lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization.

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

[IEEE ICRA'23] A new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction.

ImMesh: An Immediate LiDAR Localization and Meshing Framework

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

A real-time, direct and tightly-coupled LiDAR-Inertial SLAM for high velocities with spinning LiDARs

A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on Quatro and Nano-GICP

[T-RO 24] Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms

[IROS 2023] Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking

A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator

A Map-based localization implementation combining FAST-LIO2 as an odometry with Quatro + Nano-GICP as a map matching method

A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

A CUDA reimplementation of the line/plane odometry of LIO-SAM. A point cloud hash map (inspired by iVox of Faster-LIO) on GPU is used to accelerate 5-neighbour KNN search.

Multi-LiDAR version of FAST-LIO2: A computationally efficient and robust LiDAR-inertial odometry (LIO) package

A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on ScanContext, Quatro, and Nano-GICP

A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking.

[IROS'22] Official code repo for "A LiDAR-inertial Odometry with Principled Uncertainty Modeling"

A Map-based localization implementation combining FAST-LIO2 as an odometry with Quatro + Nano-GICP as a map matching method, and with ScanContext as a loop candidate detection method