shourenzhong Goto Github PK

6-dof-inertial-odometry

IMU-Based 6-DOF Odometry

apollo

An open autonomous driving platform

c-

cplusplusthings

C++那些事

deep-learning-localization-mapping

A collection of deep learning based localization models

deepvo-pytorch

PyTorch Implementation of DeepVO

df-vo

Depth and Flow for Visual Odometry

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

efficientnet-pytorch

A PyTorch implementation of EfficientNet

evo

Python package for the evaluation of odometry and SLAM

flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

lhy_dl_hw

lstr

This is an official repository of End-to-end Lane Shape Prediction with Transformers.

ml-notes

notes about machine learning

mpcc

Model Predictive Contouring Controller (MPCC) for Autonomous Racing

msckf_mono

VIO_Learning. A simple implement of mono msckf in https://github.com/TurtleZhong/msckf_mono/tree/master/src/msckf_mine_1.0

msckf_mono-1

Monocular MSCKF ROS Node

msckf_vio

Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

robot_localization

robot_localization is a package of nonlinear state estimation nodes. The package was developed by Charles River Analytics, Inc. Please ask questions on answers.ros.org.

rpg_trajectory_evaluation

Toolbox for quantitative trajectory evaluation of VO/VIO

sc-sfmlearner-release

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video (NeurIPS 2019)

se2lam

Visual-Odometric On-SE(2) Localization and Mapping (ICRA 2019)

selective_sensor_fusion

Webpage for CVPR19 paper

semantic_slam

Real time semantic slam in ROS with a hand held RGB-D camera

sfmlearner

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

sfmlearner-pytorch

Pytorch version of SfmLearner from Tinghui Zhou et al.

simplenes

An NES emulator in C++

torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

vehiclecan_gt_acquisition

vinet

VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem