Open-source alternatives to StaticMapping

Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration

Advanced implementation of LOAM

This repository maintains the implementation of "Event-based Stereo Visual Odometry".

VINS-Fusion is a multi-sensor fusion framework and visual-inertial odometry system. It integrates camera images, inertial measurement unit data, and global positioning signals through a non-linear optimization system to track the position and orientation of autonomous vehicles. The system includes a visual loop closure engine that utilizes a bag-of-words approach to recognize previously visited locations and correct trajectory drift. It further provides tools for online spatio-temporal calibration to determine the physical offset and time synchronization between cameras and inertial sensors d

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)

Direct Sparse Odometry

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

pyslam is a framework for Simultaneous Localization and Mapping that combines Python flexibility with C++ performance. It is a sparse SLAM implementation designed to map environment geometry and track device location by processing image frames into 3D points. The project features a bridge for exposing high-performance C++ classes to Python scripts using zero-copy memory sharing. This integration allows for switching between a scripting interface for rapid prototyping and a compiled core for execution speed. The system includes a spatial map optimizer to refine 3D point and camera pose estima

Atlas: End-to-End 3D Scene Reconstruction from Posed Images

Index repo for Kimera code

A lightweight, accurate and robust monocular visual inertial odometry based on Multi-State Constraint Kalman Filter.

A LiDAR odometry pipeline that just works

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

A ROS wrapper for libviso2, a library for visual odometry

LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto

This project provides a ROS package for localization of an autonomous vehicle. The localization is based on a map consisting of ORB features. The mapping and localization module is taken from the ORB-SLAM2 implementation. Our project builds on top of the ROS-enabled version. In this extensions…

Simultaneous localization and mapping using fiducial markers.

X Inertial-aided Visual Odometry

CUDA Visual Library by RPG

Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021

SLAM with apriltags

OpenVSLAM: A Versatile Visual SLAM Framework