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Great Research survival advice from my Masters thesis adviser’s Doctoral dissertation supervisor.

Just to merge different sites into one space only. I did blog on google blog spot, namely Blogger on the same categories of AI, but it’s for enthusiastic reasons only.


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Hello the SCIENTIFIC world

I resume using WordPress because many mathematicians and computer scientist use it to share and teach the mass about their research work. They even started massive open online collaborative research projects (e.g. Polymath, AI.ON) a.k.a MOOCR. Personally, online learning is my favorite style due to its independent nature.

I am a life long learner, and I started learning about Mathematics and Computer Science mostly from online resources. First, as a high school student I solved maths problems on the discussion forum, now art of problem solving, in preparation for mathematical olympiad. Second, I learned about computer science from EdTech sites like Udacity, Coursera and EdX in order to fill in the gap of knowledge in CS undergraduate while doing a Masters of Science in the field.

Now, having made the decision to pursue scientific research, I turn to blog to read and solve problems on mathematician’s posts, from Terence Tao, most notably. Andrej Karpathy, a former PhD student at Stanford University is also a prolific blogger on CS research, whose blog posts and lecture notes helped me a lot in conducting my MS thesis.

Writing blog also help me in writing future research papers. WordPress supports \LaTeX, which means I can include mathematical equations as well, just like the old time on MathLinks. Actually, I did used Quora for blogging, but it’s just a site product of the Question answering site, and not many scientist uses it except to answer fans’ questions.

I am looking forward to publishing more and more meaningful and interesting blog posts in the near future, and will share with you as soon as I have something new.

Real-time SLAM for 3D Dense Mapping Using Geometric Features

Shih-Yun (Laila) Lo

The motivation of this project follows:

  • Low cost 3D sensors (such as Kinect and Xtion) becomes popular and are applied on SLAM (Simultaneous localization and mapping) tasks.
  • The large amount of 3D point cloud data produced by 3D sensors can potentially increase the quality of 3D mapping, but it requires more computational power (e.g. using GPU)
  • Using GPU for real-time SLAM consumes a lot of electric power, which is inappropriate to be implemented on stand-alone robots or portable devices.

We mainly use PCL(point cloud library) in C++ in this project,  on ROS on Ubuntu 14.04. The system pipeline follows as:


Feature Exaction: based on the depth information from raw data, we down sampled and locally exactly feature points that are geometrically significant, such as points with low curvatures in the surrounding environments, consistent surface normals, or high variation in curvatures. We therefore are capable to categorized points into planes or…

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Continually aim just beyond your current range

A successful individual typically sets his next goal somewhat but not too much above his last achievement. In this way he steadily raises his level of aspiration. (Kurt Lewin) Among chess players, …

Nguồn Continually aim just beyond your current range

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