I know this a very vague question but I really need to know this.
Can anyone help me finding a kinda of matrix that shows how much time spent by different companies writing their map tile cutters/renderers like Google,Bing,Yahoo, OpenStreetMap, Maptiler, Mapnik (at the heart of TileMill) and Geoserver tile rendering system.
Since I need to make a rough estimate that if I start writing the entire tile cutting and rendering mechanism in house then how much time,resources and money do I need.
Will appreciate all the answers.
Related
I need some help in generating a map.
Instead of taking one hundred screenshots of areas and trying to perfectly combine them in photoshop, I wonder if there is a script/software that can export me a big whole area with the same level of detail as I zoom-in. I assume it will be a ~50,000x50,000px image but i really don't mind.
The only thing I really tried is to take multiple screenshots and combine them but I need to work on a big canvas, program crashes or sometimes the whole project is messing up when arranging.
Hi I am training reinforcement learning agents for a control problem using PPO algorithm. I am tracking the accumulated rewards for each episode during the training process. Several times during the training process I see a sudden dip in the accumulated rewards. I am not able to figure out why this is happening or how to avoid this. Tried with changing some of the hyper parameters like changing the number of neurons in the neural network layers, learning rate etc.. but still I see this happening consistently.
If I debug and check the actions that are being taken during dips, obviously actions are very bad hence causing a decrease in rewards.
Can some one help me with understanding why this is happening or how to avoid this ?
Some of plots of my training process
I recently read this paper: https://arxiv.org/pdf/1805.07917.pdf
I haven't used this method in particular, so I can't really vouch for the usefulness, but the explanation to this problem seemed convincing to me:
For instance, during the course of learning, the cheetah benefits from leaning forward to increase its speed which gives rise to a strong gradient in this direction. However, if the cheetah leans too much, it falls over. The gradient-based methods seem to often fall into this trap and then fail to recover as the gradient information from the new state has no guarantees of undoing the last gradient update.
I'm asking a question for a school project. I am trying to play a video in a loop and change its speed according to the distance from the Kinect 1414. When you are far the video plays at normal speed, which then increases as you get closer.
I tried in different ways but sometimes either the video doesn't loop or it doesn't show, I can only hear the audio. Other solutions don't let me change the speed of the video. Do you know any way to have the distance of a person affect the video?
Thanks.
Stack Overflow isn't designed for general "how do I do this" type questions like this. It's for specific "I tried X, expected Y, but got Z instead" type questions. But I'll try to help in a general sense.
You need to break your problem down into smaller pieces and then take on those pieces one at a time. For example, can you start with a very basic sketch that just displays the distance from the Kinect? Don't worry about the video yet, just display the distance on the screen.
Separately from that, can you create another sketch that just shows a video playing in a loop? Work your way up from there: can you make it so its speed is based on a hard-coded value? How about on a value like mouseX?
Get those two basic sketches working perfectly by themselves before you start thinking about combining them. Then if you get stuck on one of those steps, you can post a MCVE along with a specific question (in a new question post), and we'll go from there. Good luck.
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]
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.]
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.
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]
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!
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:
Firstly, the robot should make a large number of random guesses (called particles) as to where it could possibly be within its known environment.
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 360ยบ 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!
I have a course in my current semester in which I'm required to do a project on application of AI. I have decided to do this on game AI. I have 2 basic ideas: implementing an FPS bot(s) or implementing soccer AI.
I'm quiet a noob at AI right now, I've implemented basic pathfinding algos (A*, etc), and have studied about Finite state machines, some First Order logic, basic Neural Network stuff(Backpropagation ALgo), and am currently doing a course on Genetic Algorithms.
Our main focus is on the bot right now. Our plans include:
Each 'bot' would be implemented using a Finite State Machine (FSM), which would contain the possible states the bot could have; & the rules for the action/state changes that are going to take place when it receives an input.
In bot group movement, each bot would decide whether to strike, ways to strike; based on range, number of bots, existing fights using Neural Networks.
By using genetic algorithms the opponents next move could be anticipated based on repetitive moves.
Although I've programmed a few 2d games till now in my free time (like pacman, tetris, etc), I've never really gone into the 3d area. We will most probably be using a 3d engine.
We want to concentrate most of our energy on the AI part. We would like not to be bothered with unnecessary details about the animation/3d models, etc. For example, if we could find a framework which has functions like Moveright() which just moves the bot to the right, it would be really awesome.
My basic question is : is it too ambitious to go about it in the way we have planned, considering the duration of the project is abour 3 months? Should we go 3d and use a 3d game engine? is it easy to use such engines, if you have no experience with them before? If yes, what kind of engine would be suitable to our project?
I came accross another idea, given in the book AI Game programming by example, where the player would have a top down view of the bots. Would that way be more appropriate?
Thanks .. sorry about the length of the question .. it's just that my problem is a bit too specific.
My basic question is : is it too
ambitious to go about it in the way we
have planned, considering the duration
of the project is abour 3 months?
Yes -- but that's not necessarily a bad thing :)
Should we go 3d and use a 3d game
engine?
No. Mainly because you said:
We want to concentrate most of our
energy on the AI part.
Here's what I'd do, based on my experience (and knowing that, as a student, I often bit off way more than I could chew, too):
Make your simulation function irrespective of a graphical component. Have it publish "updates" to another layer, that consist of player and ball vectors. By doing so you'll be keeping your AI tasks separate from everything else, which means you have fewer bugs to worry about, and you can also unit test your underlying simulation much easier.
Take those "updates" and create your first "visualization" layer -- make it the simplest 2D representation possible. It could just be a stream of text lines: "Player 1 has the ball / Player 1 kicked ball at (30,40) with speed 20kph". That will be hard enough for your first pass since you'll be figuring out how to take data published by the simulation and doing something with it.
Your next visualization might add a 2D grid of ANSI graphics (think rogue-like) to actually show players and the ball moving. Your next one after that might be sprites. And so on. Note how you incrementally increase the complexity of your visualization... don't make your first step go to using a technology (3d graphics engine) you've never used before. (You'll never finish your project in that case.)
As for your questions about which route to take -- FSMs, NNs, GAs, top-down design -- you should rank your interest in them from most to least (along with the rest of your group) and then tackle them, in that order. You might consider doing one style for one team and a different design for the other team. You might want to make your FSM team play against a FSM team that's had an additional tweak done to it, in order to compare and contrast if you think your changes are actually being beneficial (you might be surprised and find out they make the team worse). Actually, that's where unit testing and splitting the simulation from the visualization come in very, very handy -- you should be able to "sim" as many games as you need to to get experimental results without worrying about graphics. You might even do it in batches overnight with scripts.
In general, my advice to you is this: break down your project into the tiniest pieces you can, and tackle them one at a time, so no matter where you're at when time runs out, you'll have something interesting to show off.
You could have a look at guntactyx, that's what I had to use when I did my AI unit at uni.
It takes care of all the display, physics, sound etc... for you, all you have to do is program your team of bots.
The API includes functions to make the bot move left or right, shoot, hear sounds (like gun shots) etc... and it comes with a few sample bots so you don't start from scratch.
Also, it's quite fun to watch your bots battling your friends' bots :)