LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

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

A LiDAR odometry pipeline that just works

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

[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.

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

🌟 SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations (ICRA 2023)

📍PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency [TRO' 24]

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

A LiDAR odometry pipeline for wheeled mobile robots

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

SNAP: Self-supervised Neural Maps for Visual Positioning and Semantic Understanding (NeurIPS 2023)

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.

A tool to automatically create 3D mappings of lights from two videos

[ROS2 humble] Convert 3D LiDAR map to 2D Occupancy Grid Map

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

3D GIS SDK for MGP engine

Yahboom ROS Transbot Robot with Lidar Depth camera support MoveIt 3D mapping for Nvidia Jetson NANO 4GB B01

Crafting 3D maps of Antarctica with PyGMT and the new IBCSO V2 data

The goal of this project is to build a robot capable of mapping its environment in a 3D simulation view. It uses a neural network for depth estimation deployed on a Jetson Nano. The Jetson is also connected to an Arduino Nano to get the gyro data from its IMU to project the depth values in a 3D world based on the orientation of the robot.