I have a question related to change detection. Application domain is robotics/planning.

Background/setting:

There is a sensor detecting distance from obstacle (ultrasonic / sonar sensor) at a specific position (x, y, theta) in the environment.

It returns some reading at regular time intervals. Lets say the reading is R and over a period of time it records R+ or R- (+/- means variation due to sensor inaccuracies).

Case 1: I introduce an additional object between the sensor and the obstacle at a distance D (D < R) so that at the next instance D is detected and returned

Case 2: I remove the original obstacle and now the next obstacle is D' (D' > R) and at the next instance D' is returned.

** Question **

Is there a way to exactly (or with high probability) say that a changed occurred NOW (when I add or remove an obstacle)?

Most change analysis algorithms consider a run length before change point and some data after change point and indicate the position change occurred.

But none I have read so far say change happened NOW; even the "online" algorithms seem to need some burn in data.

I have a question related to change detection. Application domain is robotics/planning.

Background/setting:

There is a sensor detecting distance from obstacle (ultrasonic / sonar sensor) at a specific position (x, y, theta) in the environment.

It returns some reading at regular time intervals. Lets say the reading is R and over a period of time it records R+ or R- (+/- means variation due to sensor inaccuracies).

Case 1: I introduce an additional object between the sensor and the obstacle at a distance D (D < R) so that at the next instance D is detected and returned

Case 2: I remove the original obstacle and now the next obstacle is D' (D' > R) and at the next instance D' is returned.

Question

Is there a way to exactly (or with high probability) say that a changed occurred NOW (when I add or remove an obstacle)?

Most change analysis algorithms consider a run length before change point and some data after change point and indicate the position change occurred.

But none I have read so far say change happened NOW; even the "online" algorithms seem to need some burn in data.

EDIT:

Ultimate goal

I want to implement a method that takes the data vector and return if the latest data point was a change point.

A possible Solution/hack

Since my work involves streaming data, this is the approach I am currently taking.

1. Read a window of data (for now, my window size is 20 values) from the end of the stream.
2. Run bcp (from R) on this window.
3. Check for the posterior probability of the change at location 18. (for all the runs i just had, the last value is NA, hence ignore that, and the data is zero indexed, (calling R from Python using rpy2), hence, the position turns out 18 for window size of 20.
4. Set a threshold of 70% for the posterior probability (for now in my experimental setting this works fine, I may have to work on getting a proper threshold later)
5. If the posterior probability at location 18 > 70%, I return TRUE indicating the recent data point has a different mean, or "change detected", else return FALSE.

This may not be the most efficient way of doing it, but it is doing its job for now. I am using this approach to carry my work forward.

I will update the thread if I find a better approach.

Thanks you all for the help!