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2048-deep-reinforcement-learning

Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning

2dscanmatching_slam

Set of algorithms for 2D scan matching. Comparison of the state of the art.

3d-bonet

3D-BoNet in Tensorflow (NeurIPS 2019, Spotlight)

active-orb-slam2

ORB SLAM2 with Active Planning

apollometricprocessing

Scripts used for the creation of 3D data from Apollo [15-17] Metric imagery using Ames Stereo Pipeline

aprilsam

SLAM optimization algorithm

aruco_ekf_slam

ArUco-based EKF-SLAM.

autonomous-learning-library

A PyTorch library for building deep reinforcement learning agents.

autoware

Open-source software for self-driving vehicles

awesome-robotics-libraries

:sunglasses: A curated list of robotics libraries and software

awesome-slam

A curated list of awesome SLAM tutorials, projects and communities.

awesome-slam-1

A curated list of resources of SLAM

awesome-slam-datasets

A curated list of awesome datasets for SLAM

awesome-slam-list

awesome-SLAM-list

awesome-visual-slam

:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers

bdd_driving_model

bert-pytorch

Google AI 2018 BERT pytorch implementation

bht-ams-playerstage

Player/Stage SLAM

breezyslam

Simple, efficient, open-source package for Simultaneous Localization and Mapping

btraj

Bezier Trajectory Generation for Autonomous Quadrotor, ICRA 2018

c-slam

C-SLAM

carrotslam

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

cartographer-1

关于谷歌slam地图库cartographer的源码注释

cartographer_fetch

Provides Fetch integration for Cartographer.

cartographer_ros

Provides ROS integration for Cartographer.

ccm_slam

CCM-SLAM: Robust and Efficient Centralized Collaborative Monocular SLAM for Robotic Teams

ceres-solver

A large scale non-linear optimization library

cmvs-pmvs

This software (CMVS) takes the output of a structure-from-motion (SfM) software as input, then decomposes the input images into a set of image clusters of managable size. An MVS software can be used to process each cluster independently and in parallel, where the union of reconstructions from all the clusters should not miss any details that can be otherwise obtained from the whole image set. CMVS should be used in conjunction with an SfM software Bundler and an MVS software PMVS2 (PMVS version 2).

cnn-slam

The combination of CNN and SLAM, aiming to achieve a better result of traditional geometric-based SLAM architecture.