Trista's repositories

Deep-Learning-Interview-Book

深度学习面试宝典(含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向)

git-cheat-sheet

:octocat: git and git flow cheat sheet

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A_LOAM_Detailed_Comments

A_LOAM的详细注释版本

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apollo_previous

An open autonomous driving platform

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autoware.ai

Open-source software for self-driving vehicles

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awesome-visual-slam

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

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BundleFusion

[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

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colour-demosaicing

CFA (Colour Filter Array) Demosaicing Algorithms for Python

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CppPrimer

:books: Solutions for C++ Primer 5th exercises.

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digital_image_process

数字图像处理学习笔记

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hdl_graph_slam

3D LIDAR-based Graph SLAM

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interview_internal_reference

2020年最新总结,阿里,腾讯,百度,美团,头条等技术面试题目,以及答案,专家出题人分析汇总。

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learning-cmake

learning cmake

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Lee-SLAM-source

SLAM 开发学习资源与经验分享

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LeetCodeAnimation

Demonstrate all the questions on LeetCode in the form of animation.(用动画的形式呈现解LeetCode题目的思路)

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LIO-SAM

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

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lsd_slam

LSD-SLAM

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ManhattanSLAM

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

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ndt_omp

Multi-threaded and SSE friendly NDT algorithm

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ORB_SLAM2_SSD_Semantic

动态语义SLAM 目标检测+VSLAM+光流/多视角几何动态物体检测+octomap地图+目标数据库

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ORB_SLAM3_detailed_comments

Detailed comments for ORB-SLAM3

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pcl

Point Cloud Library (PCL)

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relative_pose

A Collection of Algorithms for Relative Pose Estimation of a Calibrated Camera

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rtabmap

RTAB-Map library and standalone application

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rtabmap_ros

RTAB-Map's ROS package.

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semantic_suma

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

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SLAM-BOOK

这是一本关于SLAM的书稿,希望能清楚的介绍SLAM系统中的使用的几何方法和深度学习方法。书稿最后应该会达到200页左右,书稿每章对应的代码也会被整理出来。

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SP-SLAM

Point-Plane SLAM Using Supposed Planes for Indoor Environments

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VLOAM-CMU-16833

CMU 16-833 "Robot Localization and Mapping" Course Project

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