jianqiang03

A robotic researcher

Pinned Repositories

A-LOAM
Advanced implementation of LOAM
Language:C++00
a-loam-noted
Language:C++00
aanet
AANet: Adaptive Aggregation Network for Efficient Stereo Matching, CVPR 2020
Language:Python00
ai-notebooks
📚 Some notebooks implementing AI algorithms
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algorithms
Bug-tracking for Jeff's algorithms book, notes, etc.
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apollo
An open autonomous driving platform
Language:C++00
Autoware
Open-source software for self-driving vehicles
Language:C++00
blam
Language:Shell1 1 00
hdl_graph_slam
3D LIDAR-based Graph SLAM
Language:C++1 1 00
vins_fusion_noted
Language:C++11

jianqiang03's Repositories

jianqiang03/vins_fusion_noted
Language:C++11
jianqiang03/a-loam-noted
Language:C++00
jianqiang03/aanet
AANet: Adaptive Aggregation Network for Efficient Stereo Matching, CVPR 2020
Language:Python00
jianqiang03/ai-notebooks
📚 Some notebooks implementing AI algorithms
00
jianqiang03/apollo
An open autonomous driving platform
Language:C++00
jianqiang03/cartographer_detailed_comments_ws
cartographer work space with detailed comments
jianqiang03/darknet_ros
YOLO ROS: Real-Time Object Detection for ROS
jianqiang03/DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
jianqiang03/depth_clustering
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
1
jianqiang03/dynamixel-workbench
ROS Packages for Dynamixel Workbench
Language:C++
jianqiang03/ekf_state_estimation
A python implementation of es_ekf for state estimation
Language:Python
jianqiang03/leetcode
A leetcode conclusion
jianqiang03/lio-mapping
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
jianqiang03/LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
jianqiang03/lio-sam-noted
jianqiang03/msckf_mono
Monocular MSCKF ROS Node
Language:C++1 0
jianqiang03/ORBSLAM3_NOTED
jianqiang03/pseudo_lidar
(CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
jianqiang03/range-mcl
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps
jianqiang03/SC-A-LOAM
Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
jianqiang03/scan_match_ros
Language:Makefile
jianqiang03/segmap
A map representation based on 3D segments
Language:C++
jianqiang03/semantic_suma
Semantic Mapping using Surfel Mapping and Semantic Segmentation (Chen et al IROS 2019)
Language:C++1 0
jianqiang03/sort_algorithm
Language:C++
jianqiang03/tensorflow_object_detector
Tensorflow Object Detector
Language:Jupyter Notebook1 0
jianqiang03/velodyne
ROS support for Velodyne 3D LIDARs http://ros.org/wiki/velodyne
Language:C++
jianqiang03/VINS-Mono
A Robust and Versatile Monocular Visual-Inertial State Estimator
jianqiang03/VINS-Mono-noted
jianqiang03/VO-SLAM-Review
SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects
jianqiang03/votenet
Deep Hough Voting for 3D Object Detection in Point Clouds