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edge_alignment

Ros package for Edge Alignment with Ceres solver

ego-planner-swarm

egomotion_with_local_loop_closures

This code is based on the paper [Computing Egomotion with Local Loop Closures for Egocentric Videos](https://arxiv.org/pdf/1701.04743.pdf) by Suvam Patra, Himanshu Aggarwal, Himani Arora, Subhashis Banerjee, Chetan Arora. It implements a robust method for camera pose estimation using short local loop closures and rotation averaging.

elevation_mapping

Robot-centric elevation mapping for rough terrain navigation

eskf-attitude-estimation

Error-State KF algorithm to estimate attitude

event-based_vision_resources

fbow

FBOW (Fast Bag of Words) is an extremmely optimized version of the DBow2/DBow3 libraries.

fine-to-coarse-registration

Implementation of the Fine-To-Coarse Registration algorithm:

flame-detection-system

A video-based multi-feature flame detection system.

flownet3d

FlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)

g2o_pose_optimization

Use g2o to optimize the camera pose. Initial guess is from EPnP.

g2o_tutorial

simple tutorial for g2o using ROS

g2o_tutorial-1

ros indigo version of g2o_tutorial

gait-tracking-with-x-imu

gms-feature-matcher

C++ code for "GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence"

go-icp

Implementation of the Go-ICP algorithm for globally optimal 3D pointset registration

goturn

Source code for paper: Learning to Track at 100 FPS with Deep Regression Networks, Held, et al. ECCV 2016

gps_visual_odometry

gpslam

Sparse Gaussian Processes for SLAM

gpu_computing_in_robotics

grid_map

Universal grid map library for mobile robotic mapping

hard_sync_dut

hdl_graph_slam

3D LIDAR-based Graph SLAM

hdl_graph_slam_noted

hdl_graph_slam 简单中文注释版，如果有错欢迎在issue处讨论，指出

hdrfusion

A C++ implementation of our paper "HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor"

homographytrackingdemo

homography tracking

ice-ba

icpslam

A basic SLAM system that employs 2D and 3D LIDAR measurements

iicp

Visual Odometry using Intensity asssited ICP

image-super-resolution

Super-scale your images and run experiments with Residual Dense and Adversarial Networks.