lidar-odometry

Track Advancement of SLAM 跟踪SLAM前沿动态【2021 version】

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

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

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

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

ICRA 2021 - Robust Place Recognition using an Imaging Lidar

Self-supervised Deep LiDAR Odometry for Robotic Applications

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

This dataset is captured using a Velodyne VLP-16, which is mounted on an UGV - Clearpath Jackal, on Stevens Institute of Technology campus

A simple localization framework that can re-localize in built maps based on FAST-LIO.

Easy description to run and evaluate Lego-LOAM with KITTI-data

Easy description to run and evaluate A-LOAM with KITTI-data

LIDAR SLAM for Autonomous Vehicles Playground

A modular software architecture for Automatic Plant Phenotyping

The Light Imaging Detection and Ranging (LIDAR) is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor. The LIDAR Sensor escalates the entire mechanism with great efficiency which is notified with process and main activation codes.