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I’ve been spending a lot of time working with inertial measurement units recently and am discovering the surprising amount of mathematics that goes into using data from accelerometers and gyroscopes to get the orientation of an object in 3D space. The story begins with me trying to integrate an angular velocity vector (in 3D) to get the orientation of an object. Angular velocity is a vector but common representations of orientation (like Euler Angles) are not.

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When prototyping programs that deal with lots of data on an Arduino and other embedded systems or even on full blown computers, it’s really useful to have a quick tool for plotting the output of the program. Initially, I used python for doing this. Python is a beautifully simple language and between Numpy, Scipy and Matplotlib, you can do pretty much anything you want with data; from doing simple plotting to running machine learning algorithms on the data.

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Over the past few months I’ve been spending a lot of time on implementing various signal processing algorithms in C/C++. Things like Kalman Filters, various types of FIR filters and finite state machines. The number of steps needed to implement each these algorithms were fairly small and in the beginning I tried to put all the functionality of these implementations into simple to use C++ classes. This made things look neater and also fit in quite well with the Arduino programming framework (I was implementing a lot of these algorithms on Arduino compatible microcontrollers like the Teensy).

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The IEEE International Conference on Robotics and Automation (ICRA) that happened in Singapore over the last week is often referred to as the robotics conference. If you’re an academic working in the field of robotics, Singapore was the place to be in the last week. So I spent most of my time hanging around the Marina Bay Sands Hotel Convention center as a student volunteer for the conference, helping out and - in my free time - attending some of the hundreds of presentations that that took place.

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