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awesome-slam

A curated list of SLAM resources

awesome-slam-datasets

A curated list of awesome datasets for SLAM

basicfeaturedetection

SIFT, SURF, ORB

bilateral-filtering

Applying Bilateral Filtering to images

cape

Cylinder and Plane Extraction from Depth Cameras

collaborativeslamdataset

The dataset associated with our ISMAR 2018 paper on Collaborative SLAM.

cpi

Closed-form Preintegration for Graph-based Visual-Inertial Navigation

d435i_rgbd_imu_data

Retrieve the RGBD data and IMU data from D435i.

dloopdetector

Fast loop detector for sequences of monocular images

dpd-project

dpptam

DPPTAM: Dense Piecewise Planar Tracking and Mapping from a Monocular Sequence

dvo_slam

Dense Visual Odometry and SLAM

evaluate_ate_scale

Modified tool of the TUM RGB-D dataset that automatically computes the optimal scale factor that aligns trajectory and groundtruth. Useful to evaluate monocular VO/SLAM.

fastfusion

Dataset released

featurecompare

Using opencv3.3.1&C++ to compare ORB、SIFT、SURF、FAST features detect algorithms.

featuremethod

A demo for visual odometry in feature method using OpenCV.

image-processing

Code to implement bilateral and joint bilateral filters

image-processing-joint-bilateral-filter

This is an implementation of the Joint Bilateral Filter in python using openCV

infinitam

A Framework for the Volumetric Integration of Depth Images

learn-orb-vio-stereo-mono

Stereo and Mono VIO, Visual Inertial, ORB-SLAM2

learnviorb

lee-slam-source

SLAM 开发学习资源与经验分享

linefit_ground_segmentation

Ground Segmentation

living

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maplab

An open visual-inertial mapping framework.

multiplane-extraction

Based on Region Growing and RANSAC, the project can extract series of planes from on single PCD/PLY document

opvo

Sample code of BMVC 2017 paper: "Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities"

orb-slam-plane

orb-slam2_commentversion

吴博版本：ORB-SLAM2_CommentVersion

orb-slam2_rgbd_dense_map

This repository is modified from Xiang Gao's "ORB_SLAM2_modified".It is added a dense loopclosing map model.