# Publications

. GMC: Grid Based Motion Clustering in Dynamic Environment. IntelliSys, 2019.

. Heading Reference-Assisted Pose Estimation for Ground Vehicles. IEEE Transactions on Automation Science and Engineering, 2018.

. Ultra-wideband aided fast localization and mapping system. IROS, 2017.

. A Hybrid Feature Parametrization for Improving Stereo-SLAM Consistency. ICCA, 2017.

. Stereo Vision based Negative Obstacle Detection. ICCA, 2017.

. Object co-segmentation via weakly supervised data fusion. CVIU, 2017.

# Recent Posts

### How to remotely edit your project without having to use VIM

Remotely editing your work when your server does not have public IP address and you don’t want to spend any money is not so easy. Maybe you can use Team viewer or Anydesk or even chrome remote desktop, but there are high latencies. Maybe you can use ngrok to remotely ssh to your server, you have to use vim and you are not familiar with it at all 😧. I tried to use rmate but it is not convinient to edit across different files in a folder.

When you want to install a brand new Ubuntu 16.04 system. You could try to follow this guidance. Open Software & Updates and choose the fastest source. Update the system: sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list' sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116 sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt-get update sudo apt-get dist-upgrade sudo apt-get install build-essential git Install Nvidia driver sudo apt-get install nvidia-396 nvidia-settings Under cases you have Intel GPU also, please type: ### Price and spec of cloud based GPU I summarized several cloud based GPU services: Name of services Specification Price (US$)
AWS P2 instance p2.xLarge 0.9 / hour
Azure NC6 1xK80 0.9 / hour
Lambda GPU cloud 8x AWS P2 instances 0.90 / GPU/ hour
NTU HPCC 2 units of 1-P100 is scheduled to be ready by End of October 0.78 / core/ hour

### You only look once (YOLO) -- (2)

YOLO has higher localization errors and the recall (measure how good to locate all objects) is lower, compared to SSD. YOLOv2 is the second version of the YOLO with the objective of improving the accuracy significantly while making it faster. The backbone network architecture of YOLO v2 is as follows: 1. Accuracy Improvements Batch Normalization Also removes the need of dropouts. mAP increases by 2%. High-resolution Classifier To generate predictions with shape of $7\times 7 \times 125$, we replace the final fully connected layers with a $3\times 3$ convolution layer each outputting 1024 output channels.

### You only look once (YOLO) -- (1)

You Only Look Once (YOLO) is an object detection system targeted for real-time processing. There are three versions of YOLO: YOLO, YOLOv2 (and YOLO9000) and YOLOv3. For this article, we mainly focus on YOLO first stage. 1. Introduction The target is to find out the bounding box (rectangular boundary frame) of all the objects in the picture and meanwhile judge the categories of them, where left top coordinate denoted by $(x,y)$, as well as the width and height of the rectangle bounding box by $(w,h)$.