LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
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Updated
Jul 11, 2024 - C++
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.
LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.
[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.
🌟 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
[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.
[IROS 2023] Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking
Yahboom ROS Transbot Robot with Lidar Depth camera support MoveIt 3D mapping for Nvidia Jetson NANO 4GB B01
[ROS2 humble] Convert 3D LiDAR map to 2D Occupancy Grid Map
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.
This is a online social robot navigation framework that implements several techniques for that matter, like the social relevance validity checking and an extended social comfort cost function.
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