Robotic Navigation using Kinect

Question

Till now I have been able to create an application where the Kinect sensor is at one place. I have used speech recognition EmguCV (open cv) and Aforge.NET to help me process an image, learn and recognize objects. It all works fine but there is always scope for improvement and I am posing some problems: [Ignore the first three I want the answer for the fourth]

1. The frame rate is horrible. Its like 5 fps even though it should be like 30 fps. (This is WITHOUT all the processing) My application is running fine, it gets color as well as depth frames from the camera and displays it. Still the frame rate is bad. The samples run awesome, around 25 fps. Even though I ran the exact same code from the samples it wont just budge. :-( [There is no need for code, please tell me the possible problems.]

2. I would like to create a little robot on which the kinect and my laptop will be mounted on. I tried using the Mindstorms Kit but the lowtorque motors dont do the trick. Please tell me how will I achieve this.

3. How do I supply power on board? I know that the Kinect uses 12 volts for the motor. But it gets that from an AC adapter. [I would not like to cut my cable and replace it with a 12 volt battery]

4. The biggest question: How in this world will it navigate. I have done A* and flood-fill algorithms. I read this paper like a thousand times and I got nothing. I have the navigation algorithm in my mind but how on earth will it localize itself? [It should not use GPS or any kind of other sensors, just its eyes i.e. the Kinect]

Helping me will be Awesome. I am a newbie so please don't expect me to know everything. I have been up on the internet for 2 weeks with no luck.

Thanks A lot!

Answer 1

Localisation is a tricky task, as it depends on having prior knowledge of the environment in which your robot will be placed (i.e. a map of your house). While algorithms exist for simultaneous localisation and mapping, they tend to be domain-specific and as such not applicable to the general case of placing a robot in an arbitrary location and having it map its environment autonomously.

However, if your robot does have a rough (probabilistic) idea of what its environment looks like, Monte Carlo localisation is a good choice. On a high level, it goes something like:

1. Firstly, the robot should make a large number of random guesses (called particles) as to where it could possibly be within its known environment.
2. With each update from the sensor (i.e. after the robot has moved a short distance), it adjusts the probability that each of its random guesses is correct using a statistical model of its current sensor data. This can work especially well if the robot takes 360º sensor measurements, but this is not completely necessary.

This lecture by Andrew Davison at Imperial College London gives a good overview of the mathematics involved. (The rest of the course will most likely be very interesting to you as well, given what you are trying to create). Good luck!