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Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

talks

Crossing Area Multi-Robot Cooperation System 2016

Published:

We design a multi-robots cooperation system, which contains a UAV (Unmanned Aerial Vehicle) and a UGV (Unmanned Ground Vehicle). UAV system is good at long term manipulation, however, it’s weak in global navigation; UGV is flexible in global navigation but weak in endurance ability. Multi robots system could make up the shortages of different robots. However, the key problem is that, the scan from different robots may have a totally different resolution, scale, format etc. How to make the accurate cooperate localization between Large scale-Coarse point clouds with Small scale-Dense point clouds.

DJI Developer Challenge 2016

Published:

The mission is twofold, first the UAV should take the vertical taking off and landing control on a dynamic car (which is driving at 5m/s on average); Secondly, the UAV system should recognize the targets in the unstructured environment.

Field Autonomous Driving

Published:

The mission is aim to enable the Autonomous Car travel about 15~20 km in the unstructured terrain area. Because there is no obvious road shape in the terrain area, efficient road shape estimation method need to be taken; On the other side, trees and tunnel may also shield the GPS signal, so efficient LiDAR-Odometry and Visual-SLAM method needs to be added into the localization structure.

A Fusiong Framework for LiDAR-based Place Recognition Method

Published:

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places varies, the major problems come from:

  • Illumination changes, scenes under light conditions may look totally unfamiliar.
  • Appearance changes, whether or season caused appearance change, such as trees under summer and winter will show a different outlook.
  • Viewpoints changes, the same accurate pose may never occur twice, the extract features need to be viewpoints invariant, however, capture the description for a specific scene at the same time.

Conditional Invariant Feature Learning for VPR task

Published:

For the appearances changes under different weather or season conditions, we proposed a Weather condition invariant unsupervised feature extraction method. By using a conditional cycleGAN method, we achieve to split the weather conditions from the geometry features, without losing the geometry detail at the same time. The extracted weather condition invariant features could make better matching under different weather conditions. The below figure shows the reconstruction result with the extracted geometry features under different conditions. The experiment results could be seen from the below website.

LiDAR based Place Recognition

Published:

For the appearances changes under different weather or season conditions, we proposed a Weather condition invariant unsupervised feature extraction method. By using a conditional cycleGAN method, we achieve to split the weather conditions from the geometry features, without losing the geometry detail at the same time. The extracted weather condition invariant features could make better matching under different weather conditions. The below figure shows the reconstruction result with the extracted geometry features under different conditions. The experiment results could be seen from the below website.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.