Jianqiang's repositories
aanet
AANet: Adaptive Aggregation Network for Efficient Stereo Matching, CVPR 2020
ai-notebooks
📚 Some notebooks implementing AI algorithms
apollo
An open autonomous driving platform
cartographer_detailed_comments_ws
cartographer work space with detailed comments
darknet_ros
YOLO ROS: Real-Time Object Detection for ROS
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
depth_clustering
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
dynamixel-workbench
ROS Packages for Dynamixel Workbench
ekf_state_estimation
A python implementation of es_ekf for state estimation
leetcode
A leetcode conclusion
lio-mapping
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
msckf_mono
Monocular MSCKF ROS Node
pseudo_lidar
(CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
range-mcl
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps
SC-A-LOAM
Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
segmap
A map representation based on 3D segments
semantic_suma
Semantic Mapping using Surfel Mapping and Semantic Segmentation (Chen et al IROS 2019)
tensorflow_object_detector
Tensorflow Object Detector
velodyne
ROS support for Velodyne 3D LIDARs http://ros.org/wiki/velodyne
VINS-Mono
A Robust and Versatile Monocular Visual-Inertial State Estimator
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
votenet
Deep Hough Voting for 3D Object Detection in Point Clouds