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python-tutorial

A Python 3 programming tutorial for beginners.

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.

scan_match_ros

segmap

A map representation based on 3D segments

semantic_suma

Semantic Mapping using Surfel Mapping and Semantic Segmentation (Chen et al IROS 2019)

sort_algorithm

tensorflow_object_detector

Tensorflow Object Detector

vehicle_sim

velodyne

ROS support for Velodyne 3D LIDARs http://ros.org/wiki/velodyne

vim-classes

Class notes from my vim training

vim_tmux_ubuntu

A vim_tmux configuration for ubuntu

vimrc

The ultimate Vim configuration: vimrc

vins-mono

A Robust and Versatile Monocular Visual-Inertial State Estimator

vins-mono-noted

vins_fusion_noted

vio-doc

主流VIO论文推导及代码解析

visionformobilerobotics

Matlab code for assignments of the course on "Vision Algorithms for Mobile Robotics" : http://rpg.ifi.uzh.ch/teaching.html

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

zed-tensorflow

3D Object detection using the ZED and Tensorflow