HIT-orange's Stars
leovan/leovan.me
范叶亮的个人网站 | Leo Van's Website
NickeManarin/ScreenToGif
🎬 ScreenToGif allows you to record a selected area of your screen, edit and save it as a gif or video.
LarryDong/HandEye-Tsai
Handeye calibration using Tsai method. Matlab codes with real data (From camera & VICON)
XiaojingGeorgeZhang/OBCA
Optimization-Based Collision Avoidance - a path planner for autonomous navigation
IntelRealSense/librealsense
Intel® RealSense™ SDK
IRVING-L/Algorithm_fromBilibili
《B站-青岛大学-王卓老师-数据结构与算法基础》自学心得、笔记(C++语言实现)
Krasjet/quaternion
A brief introduction to the quaternions and its applications in 3D geometry.
RoboMaster/DevelopmentBoard-Examples
AlchemicRonin/-STM32-RoboMaster-
从STM32开始的RoboMaster生活系列教程
TonyZ1Min/yolo-for-k210
MichaelBeechan/ThunderNet-Review
Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.
hubery05/A-start-with-B-spline
A star path planning with b-spline trajectory smoothing
jnez71/lqRRT
Kinodynamic RRT implementation
RobotLocomotion/drake
Model-based design and verification for robotics.
RobotLocomotion/drake-external-examples
Examples of how to use Drake in your own project.
jonathancurrie/OPTI
OPTI Toolbox
symao/minimum_snap_trajectory_generation
easy sample code for minimum snap trajectory planning in MATLAB
ompugao/sandbox
xuhuairuogu/V-REP-Simulation-Projects
Learning Robotics by Playing with V-REP
DexaiRobotics/drake-torch
Example demonstrating pytorch c++ integration with drake
borglab/gtsam
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
ShuoYangRobotics/equality-constraint-LQR-compare
yajunxuejue/kinects_human_tracking
Human tracking for multi-Kinect systems around a robot on ROS
Shihao-Feng-98/RRP_Hopper_Simulation
The pybullet simulation of a RRP Hopper based on the Raibert decoupled controller.
kushuaiming/planning_algorithm
GeorgeDu/vision-based-robotic-grasping
Related papers and codes for vision-based robotic grasping
acado/acado
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization.
coin-or/Ipopt
COIN-OR Interior Point Optimizer IPOPT
PSOPT/psopt
PSOPT Optimal Control Software
turnwald/CAE_Exercise