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geodesc

Implementation of ECCV'18 paper - GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints

gf_orb_slam2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

gror_icp

GROR:A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

gslam

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.

hfnet

From Coarse to Fine: Robust Hierarchical Localization at Large Scale with HF-Net (https://arxiv.org/abs/1812.03506)

hierarchical-localization

Visual localization made easy

hitl-slam

Human-in-the-Loop SLAM

htmap

HTMap: Hierarchical Topological Mapping

image-matching-toolbox

This is a toolbox repository to help evaluate various methods that perform image matching from a pair of images.

image_undistort

A compact package for undistorting images directly from kalibr calibration files. Can also perform dense stereo estimation

iscloam

Intensity Scan Context based full SLAM implementation for autonomous driving.

larvio

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

lattice_planner

The lattice_planner package provides a move_base global planner plugin for a time-bounded A* lattice planner. The planner is designed to plan time dependent, dynamically feasible navigation paths for robots with differential drive constraints. It uses a dynamic cost map which is based on the ROS costmap representation from the costmap_2d package.

lcsd_slam

ldso

DSO with SIM(3) pose graph optimization and loop closure

lidar_align

A simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor

lio-mapping

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

lio-sam

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

lls-loam

Lidar Odometry and Mapping with Mutiple Metrics Linear Least Square ICP

loftr

Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

map-based-visual-localization

A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.

mapandlocation

定位及高精地图技术文档

mp2p_icp

Multi primitive-to-primitive (MP2P) ICP algorithms in C++

odom_extrinsic_calibrate

Odom intrinsic and odom-sensors extrinsic calibration on SE2 using batch optimization.

open_vins

An open source platform for visual-inertial navigation research.

openvslam

OpenVSLAM: A Versatile Visual SLAM Framework

orb-slam2-dualcam

Modified ORB-SLAM2 to run on dual cameras (without common-view)

orbslamm

pixloc

Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (CVPR 2021)