– Sharp ideas for the software side of robotics

Sharp ideas for the software side of robotics

Entries for the ‘Fundamentals’ Category

Introduction to the Kalman Filter, Part II

As discussed in Part I, the Kalman filter is an optimal, prediction/correction estimation technique which minimizes estimated error and can be used a wide array of problems.  While Part I focused on a one-dimensional problem space to more easily convey the underlying concepts of the Kalman filter, Part II will now expand the discussion to […]

Fundamentals: Vectors, Matrices and Matrix Operations

Our initial introduction to the Kalman filter was easy to understand because both the motion and measurement models were assumed to be one-dimensional. That’s great if you’re a lustrous point in Lineland, but the three dimensional world must be dealt with sooner or later. Specifically, within the initial introduction, location (or state) x, the control […]

Introduction to the Kalman Filter, Part I

Dealing with the real world is rough. (With an opening like that, you can probably guess how my high school days went.) To be clear here, we’re talking about robots having to deal with the real world (not me, luckily). It’s hard enough for our little silicon brethren to have limited sensory capabilities, but on […]

Fundamentals: Coordinate Frames

The world of robotics has a dizzying number of subjects; it’s quite overwhelming at first glance to figure out which topics someone “really needs to get” and which topics require a more cursory understanding. Accordingly, this will be the first in a number of posts (“number” being linearly proportional to my motivation) that I will […]


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